llm_mcp_docs.txt

# Example Clients
Source: https://modelcontextprotocol.io/clients

A list of applications that support MCP integrations

This page provides an overview of applications that support the Model Context Protocol (MCP). Each client may support different MCP features, allowing for varying levels of integration with MCP servers.

## Feature support matrix

<div id="feature-support-matrix-wrapper">
  {/* prettier-ignore-start */}

  | Client                                           | [Resources] | [Prompts] | [Tools] | [Discovery] | [Sampling] | [Roots] | Notes                                                                                           |
  | ------------------------------------------------ | ----------- | --------- | ------- | ----------- | ---------- | ------- | ----------------------------------------------------------------------------------------------- |
  | [5ire][5ire]                                     | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [AgentAI][AgentAI]                               | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Agent Library written in Rust with tools support                                                |
  | [AgenticFlow][AgenticFlow]                       | ✅           | ✅         | ✅       | ✅           | ❌          | ❌       | Supports tools, prompts, and resources for no-code AI agents and multi-agent workflows.         |
  | [Amazon Q CLI][Amazon Q CLI]                     | ❌           | ✅         | ✅       | ❓           | ❌          | ❌       | Supports prompts and tools.                                                                     |
  | [Apify MCP Tester][Apify MCP Tester]             | ❌           | ❌         | ✅       | ✅           | ❌          | ❌       | Supports remote MCP servers and tool discovery.                                                 |
  | [Augment Code][AugmentCode]                      | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools in local and remote agents.                                                      |
  | [BeeAI Framework][BeeAI Framework]               | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools in agentic workflows.                                                            |
  | [BoltAI][BoltAI]                                 | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [Claude.ai][Claude.ai]                           | ✅           | ✅         | ✅       | ❌           | ❌          | ❌       | Supports tools, prompts, and resources for remote MCP servers.                                  |
  | [Claude Code][Claude Code]                       | ❌           | ✅         | ✅       | ❌           | ❌          | ❌       | Supports prompts and tools                                                                      |
  | [Claude Desktop App][Claude Desktop]             | ✅           | ✅         | ✅       | ❌           | ❌          | ❌       | Supports tools, prompts, and resources for local and remote MCP servers.                        |
  | [Cline][Cline]                                   | ✅           | ❌         | ✅       | ✅           | ❌          | ❌       | Supports tools and resources.                                                                   |
  | [Continue][Continue]                             | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Supports tools, prompts, and resources.                                                         |
  | [Copilot-MCP][CopilotMCP]                        | ✅           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools and resources.                                                                   |
  | [Cursor][Cursor]                                 | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools.                                                                                 |
  | [Daydreams Agents][Daydreams]                    | ✅           | ✅         | ✅       | ❌           | ❌          | ❌       | Support for drop in Servers to Daydreams agents                                                 |
  | [Emacs Mcp][Mcp.el]                              | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools in Emacs.                                                                        |
  | [fast-agent][fast-agent]                         | ✅           | ✅         | ✅       | ✅           | ✅          | ✅       | Full multimodal MCP support, with end-to-end tests                                              |
  | [FLUJO][FLUJO]                                   | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Support for resources, Prompts and Roots are coming soon                                        |
  | [Genkit][Genkit]                                 | ⚠️          | ✅         | ✅       | ❓           | ❌          | ❌       | Supports resource list and lookup through tools.                                                |
  | [Glama][Glama]                                   | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [GenAIScript][GenAIScript]                       | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [Goose][Goose]                                   | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [gptme][gptme]                                   | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [HyperAgent][HyperAgent]                         | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools.                                                                                 |
  | [JetBrains AI Assistant][JetBrains AI Assistant] | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools for all JetBrains IDEs.                                                          |
  | [Klavis AI Slack/Discord/Web][Klavis AI]         | ✅           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools and resources.                                                                   |
  | [LibreChat][LibreChat]                           | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools for Agents                                                                       |
  | [Lutra][Lutra]                                   | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Supports any MCP server for reusable playbook creation.                                         |
  | [mcp-agent][mcp-agent]                           | ✅           | ✅         | ✅       | ❓           | ⚠️         | ✅       | Supports tools, prompts, resources, roots, server connection management, and agent workflows.   |
  | [mcp-use][mcp-use]                               | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Support tools, resources, stdio & http connection, local llms-agents.                           |
  | [MCPHub][MCPHub]                                 | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Supports tools, resources, and prompts in Neovim                                                |
  | [MCPOmni-Connect][MCPOmni-Connect]               | ✅           | ✅         | ✅       | ❓           | ✅          | ❌       | Supports tools with agentic mode, ReAct, and orchestrator capabilities.                         |
  | [Memex][Memex]                                   | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Support tools. Also support *building and testing* MCP server, all-in-one desktop app.          |
  | [Microsoft Copilot Studio]                       | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools                                                                                  |
  | [MindPal][MindPal]                               | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools for no-code AI agents and multi-agent workflows.                                 |
  | [MooPoint][MooPoint]                             | ❌           | ❌         | ✅       | ❓           | ✅          | ❌       | Web-Hosted client with tool calling support                                                     |
  | [Msty Studio][Msty Studio]                       | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools                                                                                  |
  | [NVIDIA Agent Intelligence toolkit][AIQ toolkit] | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools in agentic workflows.                                                            |
  | [OpenSumi][OpenSumi]                             | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools in OpenSumi                                                                      |
  | [oterm][oterm]                                   | ❌           | ✅         | ✅       | ❓           | ✅          | ❌       | Supports tools, prompts and sampling for Ollama.                                                |
  | [Postman][postman]                               | ✅           | ✅         | ✅       | ❓           | ❌          | ❌       | Supports tools, resources, prompts, and sampling                                                |
  | [Roo Code][Roo Code]                             | ✅           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools and resources.                                                                   |
  | [Slack MCP Client][Slack MCP Client]             | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools and multiple servers.                                                            |
  | [Sourcegraph Cody][Cody]                         | ✅           | ❌         | ❌       | ❓           | ❌          | ❌       | Supports resources through OpenCTX                                                              |
  | [SpinAI][SpinAI]                                 | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools for Typescript AI Agents                                                         |
  | [Superinterface][Superinterface]                 | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools                                                                                  |
  | [Superjoin][Superjoin]                           | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools and multiple servers.                                                            |
  | [TheiaAI/TheiaIDE][TheiaAI/TheiaIDE]             | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools for Agents in Theia AI and the AI-powered Theia IDE                              |
  | [Tome][Tome]                                     | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools, manages MCP servers.                                                            |
  | [TypingMind App][TypingMind App]                 | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools at app-level (appear as plugins) or when assigned to Agents                      |
  | [VS Code GitHub Copilot][VS Code]                | ✅           | ✅         | ✅       | ✅           | ✅          | ✅       | Supports dynamic tool/roots discovery, secure secret configuration, and explicit tool prompting |
  | [Warp][Warp]                                     | ✅           | ❌         | ✅       | ✅           | ❌          | ❌       | Supports tools, resources, and most of the discovery criteria                                   |
  | [WhatsMCP][WhatsMCP]                             | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools for Remote MCP Servers in WhatsApp                                               |
  | [Windsurf Editor][Windsurf]                      | ❌           | ❌         | ✅       | ✅           | ❌          | ❌       | Supports tools with AI Flow for collaborative development.                                      |
  | [Witsy][Witsy]                                   | ❌           | ❌         | ✅       | ❓           | ❌          | ❌       | Supports tools in Witsy.                                                                        |
  | [Zed][Zed]                                       | ❌           | ✅         | ❌       | ❌           | ❌          | ❌       | Prompts appear as slash commands                                                                |
  | [Zencoder][Zencoder]                             | ❌           | ❌         | ✅       | ❌           | ❌          | ❌       | Supports tools                                                                                  |

  {/* prettier-ignore-end */}

  [Resources]: /docs/concepts/resources

  [Prompts]: /docs/concepts/prompts

  [Tools]: /docs/concepts/tools

  [Discovery]: /docs/concepts/tools#tool-discovery-and-updates

  [Sampling]: /docs/concepts/sampling

  [Roots]: /docs/concepts/roots

  [5ire]: https://github.com/nanbingxyz/5ire

  [AgentAI]: https://github.com/AdamStrojek/rust-agentai

  [AgenticFlow]: https://agenticflow.ai/mcp

  [AIQ toolkit]: https://github.com/NVIDIA/AIQToolkit

  [Amazon Q CLI]: https://github.com/aws/amazon-q-developer-cli

  [Apify MCP Tester]: https://apify.com/jiri.spilka/tester-mcp-client

  [AugmentCode]: https://augmentcode.com

  [BeeAI Framework]: https://i-am-bee.github.io/beeai-framework

  [BoltAI]: https://boltai.com

  [Claude.ai]: https://claude.ai

  [Claude Code]: https://claude.ai/code

  [Claude Desktop]: https://claude.ai/download

  [Cline]: https://github.com/cline/cline

  [Continue]: https://github.com/continuedev/continue

  [CopilotMCP]: https://github.com/VikashLoomba/copilot-mcp

  [Cursor]: https://cursor.com

  [Daydreams]: https://github.com/daydreamsai/daydreams

  [Klavis AI]: https://www.klavis.ai/

  [Mcp.el]: https://github.com/lizqwerscott/mcp.el

  [fast-agent]: https://github.com/evalstate/fast-agent

  [FLUJO]: https://github.com/mario-andreschak/flujo

  [Glama]: https://glama.ai/chat

  [Genkit]: https://github.com/firebase/genkit

  [GenAIScript]: https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools/

  [Goose]: https://block.github.io/goose/docs/goose-architecture/#interoperability-with-extensions

  [JetBrains AI Assistant]: https://plugins.jetbrains.com/plugin/22282-jetbrains-ai-assistant

  [LibreChat]: https://github.com/danny-avila/LibreChat

  [Lutra]: https://lutra.ai

  [mcp-agent]: https://github.com/lastmile-ai/mcp-agent

  [mcp-use]: https://github.com/pietrozullo/mcp-use

  [MCPHub]: https://github.com/ravitemer/mcphub.nvim

  [MCPOmni-Connect]: https://github.com/Abiorh001/mcp_omni_connect

  [Memex]: https://memex.tech/

  [Microsoft Copilot Studio]: https://learn.microsoft.com/en-us/microsoft-copilot-studio/agent-extend-action-mcp

  [MindPal]: https://mindpal.io

  [MooPoint]: https://moopoint.io

  [Msty Studio]: https://msty.ai

  [OpenSumi]: https://github.com/opensumi/core

  [oterm]: https://github.com/ggozad/oterm

  [Postman]: https://postman.com/downloads

  [Roo Code]: https://roocode.com

  [Slack MCP Client]: https://github.com/tuannvm/slack-mcp-client

  [Cody]: https://sourcegraph.com/cody

  [SpinAI]: https://spinai.dev

  [Superinterface]: https://superinterface.ai

  [Superjoin]: https://superjoin.ai

  [TheiaAI/TheiaIDE]: https://eclipsesource.com/blogs/2024/12/19/theia-ide-and-theia-ai-support-mcp/

  [Tome]: https://github.com/runebookai/tome

  [TypingMind App]: https://www.typingmind.com

  [VS Code]: https://code.visualstudio.com/

  [Windsurf]: https://codeium.com/windsurf

  [gptme]: https://github.com/gptme/gptme

  [Warp]: https://www.warp.dev/

  [WhatsMCP]: https://wassist.app/mcp/

  [Witsy]: https://github.com/nbonamy/witsy

  [Zed]: https://zed.dev

  [Zencoder]: https://zencoder.ai

  [HyperAgent]: https://github.com/hyperbrowserai/HyperAgent
</div>

## Client details

### 5ire

[5ire](https://github.com/nanbingxyz/5ire) is an open source cross-platform desktop AI assistant that supports tools through MCP servers.

**Key features:**

* Built-in MCP servers can be quickly enabled and disabled.
* Users can add more servers by modifying the configuration file.
* It is open-source and user-friendly, suitable for beginners.
* Future support for MCP will be continuously improved.

### AgentAI

[AgentAI](https://github.com/AdamStrojek/rust-agentai) is a Rust library designed to simplify the creation of AI agents. The library includes seamless integration with MCP Servers.

[Example of MCP Server integration](https://github.com/AdamStrojek/rust-agentai/blob/master/examples/tools_mcp.rs)

**Key features:**

* Multi-LLM – We support most LLM APIs (OpenAI, Anthropic, Gemini, Ollama, and all OpenAI API Compatible).
* Built-in support for MCP Servers.
* Create agentic flows in a type- and memory-safe language like Rust.

### AgenticFlow

[AgenticFlow](https://agenticflow.ai/) is a no-code AI platform that helps you build agents that handle sales, marketing, and creative tasks around the clock. Connect 2,500+ APIs and 10,000+ tools securely via MCP.

**Key features:**

* No-code AI agent creation and workflow building.
* Access a vast library of 10,000+ tools and 2,500+ APIs through MCP.
* Simple 3-step process to connect MCP servers.
* Securely manage connections and revoke access anytime.

**Learn more:**

* [AgenticFlow MCP Integration](https://agenticflow.ai/mcp)

### Amazon Q CLI

[Amazon Q CLI](https://github.com/aws/amazon-q-developer-cli) is an open-source, agentic coding assistant for terminals.

**Key features:**

* Full support for MCP servers.
* Edit prompts using your preferred text editor.
* Access saved prompts instantly with `@`.
* Control and organize AWS resources directly from your terminal.
* Tools, profiles, context management, auto-compact, and so much more!

**Get Started**

```bash
brew install amazon-q
```

### Apify MCP Tester

[Apify MCP Tester](https://github.com/apify/tester-mcp-client) is an open-source client that connects to any MCP server using Server-Sent Events (SSE).
It is a standalone Apify Actor designed for testing MCP servers over SSE, with support for Authorization headers.
It uses plain JavaScript (old-school style) and is hosted on Apify, allowing you to run it without any setup.

**Key features:**

* Connects to any MCP server via SSE.
* Works with the [Apify MCP Server](https://apify.com/apify/actors-mcp-server) to interact with one or more Apify [Actors](https://apify.com/store).
* Dynamically utilizes tools based on context and user queries (if supported by the server).

### Augment Code

[Augment Code](https://augmentcode.com) is an AI-powered coding platform for VS Code and JetBrains with autonomous agents, chat, and completions. Both local and remote agents are backed by full codebase awareness and native support for MCP, enabling enhanced context through external sources and tools.

**Key features:**

* Full MCP support in local and remote agents.
* Add additional context through MCP servers.
* Automate your development workflows with MCP tools.
* Works in VS Code and JetBrains IDEs.

### BeeAI Framework

[BeeAI Framework](https://i-am-bee.github.io/beeai-framework) is an open-source framework for building, deploying, and serving powerful agentic workflows at scale. The framework includes the **MCP Tool**, a native feature that simplifies the integration of MCP servers into agentic workflows.

**Key features:**

* Seamlessly incorporate MCP tools into agentic workflows.
* Quickly instantiate framework-native tools from connected MCP client(s).
* Planned future support for agentic MCP capabilities.

**Learn more:**

* [Example of using MCP tools in agentic workflow](https://i-am-bee.github.io/beeai-framework/#/typescript/tools?id=using-the-mcptool-class)

### BoltAI

[BoltAI](https://boltai.com) is a native, all-in-one AI chat client with MCP support. BoltAI supports multiple AI providers (OpenAI, Anthropic, Google AI...), including local AI models (via Ollama, LM Studio or LMX)

**Key features:**

* MCP Tool integrations: once configured, user can enable individual MCP server in each chat
* MCP quick setup: import configuration from Claude Desktop app or Cursor editor
* Invoke MCP tools inside any app with AI Command feature
* Integrate with remote MCP servers in the mobile app

**Learn more:**

* [BoltAI docs](https://boltai.com/docs/plugins/mcp-servers)
* [BoltAI website](https://boltai.com)

### Claude Code

Claude Code is an interactive agentic coding tool from Anthropic that helps you code faster through natural language commands. It supports MCP integration for prompts and tools, and also functions as an MCP server to integrate with other clients.

**Key features:**

* Tool and prompt support for MCP servers
* Offers its own tools through an MCP server for integrating with other MCP clients

### Claude.ai

[Claude.ai](https://claude.ai) is Anthropic's web-based AI assistant that provides MCP support for remote servers.

**Key features:**

* Support for remote MCP servers via integrations UI in settings
* Access to tools, prompts, and resources from configured MCP servers
* Seamless integration with Claude's conversational interface
* Enterprise-grade security and compliance features

### Claude Desktop App

The Claude desktop application provides comprehensive support for MCP, enabling deep integration with local tools and data sources.

**Key features:**

* Full support for resources, allowing attachment of local files and data
* Support for prompt templates
* Tool integration for executing commands and scripts
* Local server connections for enhanced privacy and security

### Cline

[Cline](https://github.com/cline/cline) is an autonomous coding agent in VS Code that edits files, runs commands, uses a browser, and more–with your permission at each step.

**Key features:**

* Create and add tools through natural language (e.g. "add a tool that searches the web")
* Share custom MCP servers Cline creates with others via the `~/Documents/Cline/MCP` directory
* Displays configured MCP servers along with their tools, resources, and any error logs

### Continue

[Continue](https://github.com/continuedev/continue) is an open-source AI code assistant, with built-in support for all MCP features.

**Key features:**

* Type "@" to mention MCP resources
* Prompt templates surface as slash commands
* Use both built-in and MCP tools directly in chat
* Supports VS Code and JetBrains IDEs, with any LLM

### Copilot-MCP

[Copilot-MCP](https://github.com/VikashLoomba/copilot-mcp) enables AI coding assistance via MCP.

**Key features:**

* Support for MCP tools and resources
* Integration with development workflows
* Extensible AI capabilities

### Cursor

[Cursor](https://docs.cursor.com/advanced/model-context-protocol) is an AI code editor.

**Key features:**

* Support for MCP tools in Cursor Composer
* Support for both STDIO and SSE

### Daydreams

[Daydreams](https://github.com/daydreamsai/daydreams) is a generative agent framework for executing anything onchain

**Key features:**

* Supports MCP Servers in config
* Exposes MCP Client

### Emacs Mcp

[Emacs Mcp](https://github.com/lizqwerscott/mcp.el) is an Emacs client designed to interface with MCP servers, enabling seamless connections and interactions. It provides MCP tool invocation support for AI plugins like [gptel](https://github.com/karthink/gptel) and [llm](https://github.com/ahyatt/llm), adhering to Emacs' standard tool invocation format. This integration enhances the functionality of AI tools within the Emacs ecosystem.

**Key features:**

* Provides MCP tool support for Emacs.

### fast-agent

[fast-agent](https://github.com/evalstate/fast-agent) is a Python Agent framework, with simple declarative support for creating Agents and Workflows, with full multi-modal support for Anthropic and OpenAI models.

**Key features:**

* PDF and Image support, based on MCP Native types
* Interactive front-end to develop and diagnose Agent applications, including passthrough and playback simulators
* Built in support for "Building Effective Agents" workflows.
* Deploy Agents as MCP Servers

### FLUJO

Think n8n + ChatGPT. FLUJO is an desktop application that integrates with MCP to provide a workflow-builder interface for AI interactions. Built with Next.js and React, it supports both online and offline (ollama) models, it manages API Keys and environment variables centrally and can install MCP Servers from GitHub. FLUJO has an ChatCompletions endpoint and flows can be executed from other AI applications like Cline, Roo or Claude.

**Key features:**

* Environment & API Key Management
* Model Management
* MCP Server Integration
* Workflow Orchestration
* Chat Interface

### Genkit

[Genkit](https://github.com/firebase/genkit) is a cross-language SDK for building and integrating GenAI features into applications. The [genkitx-mcp](https://github.com/firebase/genkit/tree/main/js/plugins/mcp) plugin enables consuming MCP servers as a client or creating MCP servers from Genkit tools and prompts.

**Key features:**

* Client support for tools and prompts (resources partially supported)
* Rich discovery with support in Genkit's Dev UI playground
* Seamless interoperability with Genkit's existing tools and prompts
* Works across a wide variety of GenAI models from top providers

### Glama

[Glama](https://glama.ai/chat) is a comprehensive AI workspace and integration platform that offers a unified interface to leading LLM providers, including OpenAI, Anthropic, and others. It supports the Model Context Protocol (MCP) ecosystem, enabling developers and enterprises to easily discover, build, and manage MCP servers.

**Key features:**

* Integrated [MCP Server Directory](https://glama.ai/mcp/servers)
* Integrated [MCP Tool Directory](https://glama.ai/mcp/tools)
* Host MCP servers and access them via the Chat or SSE endpoints
  – Ability to chat with multiple LLMs and MCP servers at once
* Upload and analyze local files and data
* Full-text search across all your chats and data

### GenAIScript

Programmatically assemble prompts for LLMs using [GenAIScript](https://microsoft.github.io/genaiscript/) (in JavaScript). Orchestrate LLMs, tools, and data in JavaScript.

**Key features:**

* JavaScript toolbox to work with prompts
* Abstraction to make it easy and productive
* Seamless Visual Studio Code integration

### Goose

[Goose](https://github.com/block/goose) is an open source AI agent that supercharges your software development by automating coding tasks.

**Key features:**

* Expose MCP functionality to Goose through tools.
* MCPs can be installed directly via the [extensions directory](https://block.github.io/goose/v1/extensions/), CLI, or UI.
* Goose allows you to extend its functionality by [building your own MCP servers](https://block.github.io/goose/docs/tutorials/custom-extensions).
* Includes built-in tools for development, web scraping, automation, memory, and integrations with JetBrains and Google Drive.

### gptme

[gptme](https://github.com/gptme/gptme) is a open-source terminal-based personal AI assistant/agent, designed to assist with programming tasks and general knowledge work.

**Key features:**

* CLI-first design with a focus on simplicity and ease of use
* Rich set of built-in tools for shell commands, Python execution, file operations, and web browsing
* Local-first approach with support for multiple LLM providers
* Open-source, built to be extensible and easy to modify

### HyperAgent

[HyperAgent](https://github.com/hyperbrowserai/HyperAgent) is Playwright supercharged with AI. With HyperAgent, you no longer need brittle scripts, just powerful natural language commands. Using MCP servers, you can extend the capability of HyperAgent, without having to write any code.

**Key features:**

* AI Commands: Simple APIs like page.ai(), page.extract() and executeTask() for any AI automation
* Fallback to Regular Playwright: Use regular Playwright when AI isn't needed
* Stealth Mode – Avoid detection with built-in anti-bot patches
* Cloud Ready – Instantly scale to hundreds of sessions via [Hyperbrowser](https://www.hyperbrowser.ai/)
* MCP Client – Connect to tools like Composio for full workflows (e.g. writing web data to Google Sheets)

### JetBrains AI Assistant

[JetBrains AI Assistant](https://plugins.jetbrains.com/plugin/22282-jetbrains-ai-assistant) plugin provides AI-powered features for software development available in all JetBrains IDEs.

**Key features:**

* Unlimited code completion powered by Mellum, JetBrains’ proprietary AI model.
* Context-aware AI chat that understands your code and helps you in real time.
* Access to top-tier models from OpenAI, Anthropic, and Google.
* Offline mode with connected local LLMs via Ollama or LM Studio.
* Deep integration into IDE workflows, including code suggestions in the editor, VCS assistance, runtime error explanation, and more.

### Klavis AI Slack/Discord/Web

[Klavis AI](https://www.klavis.ai/) is an Open-Source Infra to Use, Build & Scale MCPs with ease.

**Key features:**

* Slack/Discord/Web MCP clients for using MCPs directly
* Simple web UI dashboard for easy MCP configuration
* Direct OAuth integration with Slack & Discord Clients and MCP Servers for secure user authentication
* SSE transport support
* Open-source infrastructure ([GitHub repository](https://github.com/Klavis-AI/klavis))

**Learn more:**

* [Demo video showing MCP usage in Slack/Discord](https://youtu.be/9-QQAhrQWw8)

### LibreChat

[LibreChat](https://github.com/danny-avila/LibreChat) is an open-source, customizable AI chat UI that supports multiple AI providers, now including MCP integration.

**Key features:**

* Extend current tool ecosystem, including [Code Interpreter](https://www.librechat.ai/docs/features/code_interpreter) and Image generation tools, through MCP servers
* Add tools to customizable [Agents](https://www.librechat.ai/docs/features/agents), using a variety of LLMs from top providers
* Open-source and self-hostable, with secure multi-user support
* Future roadmap includes expanded MCP feature support

### Lutra

[Lutra](https://lutra.ai) is an AI agent that transforms conversations into actionable, automated workflows.

**Key features:**

* Easy MCP Integration: Connecting Lutra to MCP servers is as simple as providing the server URL; Lutra handles the rest behind the scenes.
* Chat to Take Action: Lutra understands your conversational context and goals, automatically integrating with your existing apps to perform tasks.
* Reusable Playbooks: After completing a task, save the steps as reusable, automated workflows—simplifying repeatable processes and reducing manual effort.
* Shareable Automations: Easily share your saved playbooks with teammates to standardize best practices and accelerate collaborative workflows.

**Learn more:**

* [Lutra AI agent explained](https://www.youtube.com/watch?v=W5ZpN0cMY70)

### mcp-agent

[mcp-agent] is a simple, composable framework to build agents using Model Context Protocol.

**Key features:**

* Automatic connection management of MCP servers.
* Expose tools from multiple servers to an LLM.
* Implements every pattern defined in [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents).
* Supports workflow pause/resume signals, such as waiting for human feedback.

### mcp-use

[mcp-use] is an open source python library to very easily connect any LLM to any MCP server both locally and remotely.

**Key features:**

* Very simple interface to connect any LLM to any MCP.
* Support the creation of custom agents, workflows.
* Supports connection to multiple MCP servers simultaneously.
* Supports all langchain supported models, also locally.
* Offers efficient tool orchestration and search functionalities.

### MCPHub

[MCPHub] is a powerful Neovim plugin that integrates MCP (Model Context Protocol) servers into your workflow.

**Key features:**

* Install, configure and manage MCP servers with an intuitive UI.
* Built-in Neovim MCP server with support for file operations (read, write, search, replace), command execution, terminal integration, LSP integration, buffers, and diagnostics.
* Create Lua-based MCP servers directly in Neovim.
* Inegrates with popular Neovim chat plugins Avante.nvim and CodeCompanion.nvim

### MCPOmni-Connect

[MCPOmni-Connect](https://github.com/Abiorh001/mcp_omni_connect) is a versatile command-line interface (CLI) client designed to connect to various Model Context Protocol (MCP) servers using both stdio and SSE transport.

**Key features:**

* Support for resources, prompts, tools, and sampling
* Agentic mode with ReAct and orchestrator capabilities
* Seamless integration with OpenAI models and other LLMs
* Dynamic tool and resource management across multiple servers
* Support for both stdio and SSE transport protocols
* Comprehensive tool orchestration and resource analysis capabilities

### Memex

[Memex](https://memex.tech/) is the first MCP client and MCP server builder - all-in-one desktop app. Unlike traditional MCP clients that only consume existing servers, Memex can create custom MCP servers from natural language prompts, immediately integrate them into its toolkit, and use them to solve problems—all within a single conversation.

**Key features:**

* **Prompt-to-MCP Server**: Generate fully functional MCP servers from natural language descriptions
* **Self-Testing & Debugging**: Autonomously test, debug, and improve created MCP servers
* **Universal MCP Client**: Works with any MCP server through intuitive, natural language integration
* **Curated MCP Directory**: Access to tested, one-click installable MCP servers (Neon, Netlify, GitHub, Context7, and more)
* **Multi-Server Orchestration**: Leverage multiple MCP servers simultaneously for complex workflows

**Learn more:**

* [Memex Launch 2: MCP Teams and Agent API](https://memex.tech/blog/memex-launch-2-mcp-teams-and-agent-api-private-preview-125f)

### Microsoft Copilot Studio

[Microsoft Copilot Studio] is a robust SaaS platform designed for building custom AI-driven applications and intelligent agents, empowering developers to create, deploy, and manage sophisticated AI solutions.

**Key features:**

* Support for MCP tools
* Extend Copilot Studio agents with MCP servers
* Leveraging Microsoft unified, governed, and secure API management solutions

### MindPal

[MindPal](https://mindpal.io) is a no-code platform for building and running AI agents and multi-agent workflows for business processes.

**Key features:**

* Build custom AI agents with no-code
* Connect any SSE MCP server to extend agent tools
* Create multi-agent workflows for complex business processes
* User-friendly for both technical and non-technical professionals
* Ongoing development with continuous improvement of MCP support

**Learn more:**

* [MindPal MCP Documentation](https://docs.mindpal.io/agent/mcp)

### MooPoint

[MooPoint](https://moopoint.io)

MooPoint is a web-based AI chat platform built for developers and advanced users, letting you interact with multiple large language models (LLMs) through a single, unified interface. Connect your own API keys (OpenAI, Anthropic, and more) and securely manage custom MCP server integrations.

**Key features:**

* Accessible from any PC or smartphone—no installation required
* Choose your preferred LLM provider
* Supports `SSE`, `Streamable HTTP`, `npx`, and `uvx` MCP servers
* OAuth and sampling support
* New features added daily

### Msty Studio

[Msty Studio](https://msty.ai) is a privacy-first AI productivity platform that seamlessly integrates local and online language models (LLMs) into customizable workflows. Designed for both technical and non-technical users, Msty Studio offers a suite of tools to enhance AI interactions, automate tasks, and maintain full control over data and model behavior.

**Key features:**

* **Toolbox & Toolsets**: Connect AI models to local tools and scripts using MCP-compliant configurations. Group tools into Toolsets to enable dynamic, multi-step workflows within conversations.
* **Turnstiles**: Create automated, multi-step AI interactions, allowing for complex data processing and decision-making flows.
* **Real-Time Data Integration**: Enhance AI responses with up-to-date information by integrating real-time web search capabilities.
* **Split Chats & Branching**: Engage in parallel conversations with multiple models simultaneously, enabling comparative analysis and diverse perspectives.

**Learn more:**

* [Msty Studio Documentation](https://docs.msty.studio/features/toolbox/tools)

### NVIDIA Agent Intelligence (AIQ) toolkit

[NVIDIA Agent Intelligence (AIQ) toolkit](https://github.com/NVIDIA/AIQToolkit) is a flexible, lightweight, and unifying library that allows you to easily connect existing enterprise agents to data sources and tools across any framework.

**Key features:**

* Acts as an MCP **client** to consume remote tools
* Acts as an MCP **server** to expose tools
* Framework agnostic and compatible with LangChain, CrewAI, Semantic Kernel, and custom agents
* Includes built-in observability and evaluation tools

**Learn more:**

* [AIQ toolkit GitHub repository](https://github.com/NVIDIA/AIQToolkit)
* [AIQ toolkit MCP documentation](https://docs.nvidia.com/aiqtoolkit/latest/workflows/mcp/index.html)

### OpenSumi

[OpenSumi](https://github.com/opensumi/core) is a framework helps you quickly build AI Native IDE products.

**Key features:**

* Supports MCP tools in OpenSumi
* Supports built-in IDE MCP servers and custom MCP servers

### oterm

[oterm] is a terminal client for Ollama allowing users to create chats/agents.

**Key features:**

* Support for multiple fully customizable chat sessions with Ollama connected with tools.
* Support for MCP tools.

### Roo Code

[Roo Code](https://roocode.com) enables AI coding assistance via MCP.

**Key features:**

* Support for MCP tools and resources
* Integration with development workflows
* Extensible AI capabilities

### Postman

[Postman](https://postman.com/downloads) is the most popular API client and now supports MCP server testing and debugging.

**Key features:**

* Full support of all major MCP features (tools, prompts, resources, and subscriptions)
* Fast, seamless UI for debugging MCP capabilities
* MCP config integration (Claude, VSCode, etc.) for fast first-time experience in testing MCPs
* Integration with history, variables, and collections for reuse and collaboration

### Slack MCP Client

[Slack MCP Client](https://github.com/tuannvm/slack-mcp-client) acts as a bridge between Slack and Model Context Protocol (MCP) servers. Using Slack as the interface, it enables large language models (LLMs) to connect and interact with various MCP servers through standardized MCP tools.

**Key features:**

* **Supports Popular LLM Providers:** Integrates seamlessly with leading large language model providers such as OpenAI, Anthropic, and Ollama, allowing users to leverage advanced conversational AI and orchestration capabilities within Slack.
* **Dynamic and Secure Integration:** Supports dynamic registration of MCP tools, works in both channels and direct messages and manages credentials securely via environment variables or Kubernetes secrets.
* **Easy Deployment and Extensibility:** Offers official Docker images, a Helm chart for Kubernetes, and Docker Compose for local development, making it simple to deploy, configure, and extend with additional MCP servers or tools.

### Sourcegraph Cody

[Cody](https://openctx.org/docs/providers/modelcontextprotocol) is Sourcegraph's AI coding assistant, which implements MCP through OpenCTX.

**Key features:**

* Support for MCP resources
* Integration with Sourcegraph's code intelligence
* Uses OpenCTX as an abstraction layer
* Future support planned for additional MCP features

### SpinAI

[SpinAI](https://spinai.dev) is an open-source TypeScript framework for building observable AI agents. The framework provides native MCP compatibility, allowing agents to seamlessly integrate with MCP servers and tools.

**Key features:**

* Built-in MCP compatibility for AI agents
* Open-source TypeScript framework
* Observable agent architecture
* Native support for MCP tools integration

### Superinterface

[Superinterface](https://superinterface.ai) is AI infrastructure and a developer platform to build in-app AI assistants with support for MCP, interactive components, client-side function calling and more.

**Key features:**

* Use tools from MCP servers in assistants embedded via React components or script tags
* SSE transport support
* Use any AI model from any AI provider (OpenAI, Anthropic, Ollama, others)

### Superjoin

[Superjoin](https://superjoin.ai) brings the power of MCP directly into Google Sheets extension. With Superjoin, users can access and invoke MCP tools and agents without leaving their spreadsheets, enabling powerful AI workflows and automation right where their data lives.

**Key features:**

* Native Google Sheets add-on providing effortless access to MCP capabilities
* Supports OAuth 2.1 and header-based authentication for secure and flexible connections
* Compatible with both SSE and Streamable HTTP transport for efficient, real-time streaming communication
* Fully web-based, cross-platform client requiring no additional software installation

### TheiaAI/TheiaIDE

[Theia AI](https://eclipsesource.com/blogs/2024/10/07/introducing-theia-ai/) is a framework for building AI-enhanced tools and IDEs. The [AI-powered Theia IDE](https://eclipsesource.com/blogs/2024/10/08/introducting-ai-theia-ide/) is an open and flexible development environment built on Theia AI.

**Key features:**

* **Tool Integration**: Theia AI enables AI agents, including those in the Theia IDE, to utilize MCP servers for seamless tool interaction.
* **Customizable Prompts**: The Theia IDE allows users to define and adapt prompts, dynamically integrating MCP servers for tailored workflows.
* **Custom agents**: The Theia IDE supports creating custom agents that leverage MCP capabilities, enabling users to design dedicated workflows on the fly.

Theia AI and Theia IDE's MCP integration provide users with flexibility, making them powerful platforms for exploring and adapting MCP.

**Learn more:**

* [Theia IDE and Theia AI MCP Announcement](https://eclipsesource.com/blogs/2024/12/19/theia-ide-and-theia-ai-support-mcp/)
* [Download the AI-powered Theia IDE](https://theia-ide.org/)

### Tome

[Tome](https://github.com/runebookai/tome) is an open source cross-platform desktop app designed for working with local LLMs and MCP servers. It is designed to be beginner friendly and abstract away the nitty gritty of configuration for people getting started with MCP.

**Key features:**

* MCP servers are managed by Tome so there is no need to install uv or npm or configure JSON
* Users can quickly add or remove MCP servers via UI
* Any tool-supported local model on Ollama is compatible

### TypingMind App

[TypingMind](https://www.typingmind.com) is an advanced frontend for LLMs with MCP support. TypingMind supports all popular LLM providers like OpenAI, Gemini, Claude, and users can use with their own API keys.

**Key features:**

* **MCP Tool Integration**: Once MCP is configured, MCP tools will show up as plugins that can be enabled/disabled easily via the main app interface.
* **Assign MCP Tools to Agents**: TypingMind allows users to create AI agents that have a set of MCP servers assigned.
* **Remote MCP servers**: Allows users to customize where to run the MCP servers via its MCP Connector configuration, allowing the use of MCP tools across multiple devices (laptop, mobile devices, etc.) or control MCP servers from a remote private server.

**Learn more:**

* [TypingMind MCP Document](https://www.typingmind.com/mcp)
* [Download TypingMind (PWA)](https://www.typingmind.com/)

### VS Code GitHub Copilot

[VS Code](https://code.visualstudio.com/) integrates MCP with GitHub Copilot through [agent mode](https://code.visualstudio.com/docs/copilot/chat/chat-agent-mode), allowing direct interaction with MCP-provided tools within your agentic coding workflow. Configure servers in Claude Desktop, workspace or user settings, with guided MCP installation and secure handling of keys in input variables to avoid leaking hard-coded keys.

**Key features:**

* Support for stdio and server-sent events (SSE) transport
* Per-session selection of tools per agent session for optimal performance
* Easy server debugging with restart commands and output logging
* Tool calls with editable inputs and always-allow toggle
* Integration with existing VS Code extension system to register MCP servers from extensions

### Warp

[Warp](https://www.warp.dev/) is the intelligent terminal with AI and your dev team's knowledge built-in. With natural language capabilities integrated directly into an agentic command line, Warp enables developers to code, automate, and collaborate more efficiently -- all within a terminal that features a modern UX.

**Key features:**

* **Agent Mode with MCP support**: invoke tools and access data from MCP servers using natural language prompts
* **Flexible server management**: add and manage CLI or SSE-based MCP servers via Warp's built-in UI
* **Live tool/resource discovery**: view tools and resources from each running MCP server
* **Configurable startup**: set MCP servers to start automatically with Warp or launch them manually as needed

### WhatsMCP

[WhatsMCP](https://wassist.app/mcp/) is an MCP client for WhatsApp. WhatsMCP lets you interact with your AI stack from the comfort of a WhatsApp chat.

**Key features:**

* Supports MCP tools
* SSE transport, full OAuth2 support
* Chat flow management for WhatsApp messages
* One click setup for connecting to your MCP servers
* In chat management of MCP servers
* Oauth flow natively supported in WhatsApp

### Windsurf Editor

[Windsurf Editor](https://codeium.com/windsurf) is an agentic IDE that combines AI assistance with developer workflows. It features an innovative AI Flow system that enables both collaborative and independent AI interactions while maintaining developer control.

**Key features:**

* Revolutionary AI Flow paradigm for human-AI collaboration
* Intelligent code generation and understanding
* Rich development tools with multi-model support

### Witsy

[Witsy](https://github.com/nbonamy/witsy) is an AI desktop assistant, supporting Anthropic models and MCP servers as LLM tools.

**Key features:**

* Multiple MCP servers support
* Tool integration for executing commands and scripts
* Local server connections for enhanced privacy and security
* Easy-install from Smithery.ai
* Open-source, available for macOS, Windows and Linux

### Zed

[Zed](https://zed.dev/docs/assistant/model-context-protocol) is a high-performance code editor with built-in MCP support, focusing on prompt templates and tool integration.

**Key features:**

* Prompt templates surface as slash commands in the editor
* Tool integration for enhanced coding workflows
* Tight integration with editor features and workspace context
* Does not support MCP resources

### Zencoder

[Zencoder](https://zecoder.ai) is a coding agent that's available as an extension for VS Code and JetBrains family of IDEs, meeting developers where they already work. It comes with RepoGrokking (deep contextual codebase understanding), agentic pipeline, and the ability to create and share custom agents.

**Key features:**

* RepoGrokking - deep contextual understanding of codebases
* Agentic pipeline - runs, tests, and executes code before outputting it
* Zen Agents platform - ability to build and create custom agents and share with the team
* Integrated MCP tool library with one-click installations
* Specialized agents for Unit and E2E Testing

**Learn more:**

* [Zencoder Documentation](https://docs.zencoder.ai)

## Adding MCP support to your application

If you've added MCP support to your application, we encourage you to submit a pull request to add it to this list. MCP integration can provide your users with powerful contextual AI capabilities and make your application part of the growing MCP ecosystem.

Benefits of adding MCP support:

* Enable users to bring their own context and tools
* Join a growing ecosystem of interoperable AI applications
* Provide users with flexible integration options
* Support local-first AI workflows

To get started with implementing MCP in your application, check out our [Python](https://github.com/modelcontextprotocol/python-sdk) or [TypeScript SDK Documentation](https://github.com/modelcontextprotocol/typescript-sdk)

## Updates and corrections

This list is maintained by the community. If you notice any inaccuracies or would like to update information about MCP support in your application, please submit a pull request or [open an issue in our documentation repository](https://github.com/modelcontextprotocol/modelcontextprotocol/issues).


# Contributing
Source: https://modelcontextprotocol.io/development/contributing

How to participate in Model Context Protocol development

We welcome contributions from the community! Please review our [contributing guidelines](https://github.com/modelcontextprotocol/modelcontextprotocol/blob/main/CONTRIBUTING.md) for details on how to submit changes.

All contributors must adhere to our [Code of Conduct](https://github.com/modelcontextprotocol/modelcontextprotocol/blob/main/CODE_OF_CONDUCT.md).

For questions and discussions, please use [GitHub Discussions](https://github.com/modelcontextprotocol/modelcontextprotocol/discussions).


# Roadmap
Source: https://modelcontextprotocol.io/development/roadmap

Our plans for evolving Model Context Protocol

<Info>Last updated: **2025-03-27**</Info>

The Model Context Protocol is rapidly evolving. This page outlines our current thinking on key priorities and direction for approximately **the next six months**, though these may change significantly as the project develops. To see what's changed recently, check out the **[specification changelog](/specification/2025-06-18/changelog/)**.

<Note>
  The ideas presented here are not commitments—we may solve these challenges differently than described, or some may not materialize at all. This is also not an *exhaustive* list; we may incorporate work that isn't mentioned here.
</Note>

We value community participation! Each section links to relevant discussions where you can learn more and contribute your thoughts.

For a technical view of our standardization process, visit the [Standards Track](https://github.com/orgs/modelcontextprotocol/projects/2/views/2) on GitHub, which tracks how proposals progress toward inclusion in the official [MCP specification](https://spec.modelcontextprotocol.io).

## Validation

To foster a robust developer ecosystem, we plan to invest in:

* **Reference Client Implementations**: demonstrating protocol features with high-quality AI applications
* **Compliance Test Suites**: automated verification that clients, servers, and SDKs properly implement the specification

These tools will help developers confidently implement MCP while ensuring consistent behavior across the ecosystem.

## Registry

For MCP to reach its full potential, we need streamlined ways to distribute and discover MCP servers.

We plan to develop an [**MCP Registry**](https://github.com/orgs/modelcontextprotocol/discussions/159) that will enable centralized server discovery and metadata. This registry will primarily function as an API layer that third-party marketplaces and discovery services can build upon.

## Agents

As MCP increasingly becomes part of agentic workflows, we're exploring [improvements](https://github.com/modelcontextprotocol/specification/discussions/111) such as:

* **[Agent Graphs](https://github.com/modelcontextprotocol/specification/discussions/94)**: enabling complex agent topologies through namespacing and graph-aware communication patterns
* **Interactive Workflows**: improving human-in-the-loop experiences with granular permissioning, standardized interaction patterns, and [ways to directly communicate](https://github.com/modelcontextprotocol/specification/issues/97) with the end user

## Multimodality

Supporting the full spectrum of AI capabilities in MCP, including:

* **Additional Modalities**: video and other media types
* **[Streaming](https://github.com/modelcontextprotocol/specification/issues/117)**: multipart, chunked messages, and bidirectional communication for interactive experiences

## Governance

We're implementing governance structures that prioritize:

* **Community-Led Development**: fostering a collaborative ecosystem where community members and AI developers can all participate in MCP's evolution, ensuring it serves diverse applications and use cases
* **Transparent Standardization**: establishing clear processes for contributing to the specification, while exploring formal standardization via industry bodies

## Get Involved

We welcome your contributions to MCP's future! Join our [GitHub Discussions](https://github.com/orgs/modelcontextprotocol/discussions) to share ideas, provide feedback, or participate in the development process.


# Core architecture
Source: https://modelcontextprotocol.io/docs/concepts/architecture

Understand how MCP connects clients, servers, and LLMs

The Model Context Protocol (MCP) is built on a flexible, extensible architecture that enables seamless communication between LLM applications and integrations. This document covers the core architectural components and concepts.

## Overview

MCP follows a client-server architecture where:

* **Hosts** are LLM applications (like Claude Desktop or IDEs) that initiate connections
* **Clients** maintain 1:1 connections with servers, inside the host application
* **Servers** provide context, tools, and prompts to clients

```mermaid
flowchart LR
    subgraph "Host"
        client1[MCP Client]
        client2[MCP Client]
    end
    subgraph "Server Process"
        server1[MCP Server]
    end
    subgraph "Server Process"
        server2[MCP Server]
    end

    client1 <-->|Transport Layer| server1
    client2 <-->|Transport Layer| server2
```

## Core components

### Protocol layer

The protocol layer handles message framing, request/response linking, and high-level communication patterns.

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    class Protocol<Request, Notification, Result> {
        // Handle incoming requests
        setRequestHandler<T>(schema: T, handler: (request: T, extra: RequestHandlerExtra) => Promise<Result>): void

        // Handle incoming notifications
        setNotificationHandler<T>(schema: T, handler: (notification: T) => Promise<void>): void

        // Send requests and await responses
        request<T>(request: Request, schema: T, options?: RequestOptions): Promise<T>

        // Send one-way notifications
        notification(notification: Notification): Promise<void>
    }
    ```
  </Tab>

  <Tab title="Python">
    ```python
    class Session(BaseSession[RequestT, NotificationT, ResultT]):
        async def send_request(
            self,
            request: RequestT,
            result_type: type[Result]
        ) -> Result:
            """Send request and wait for response. Raises McpError if response contains error."""
            # Request handling implementation

        async def send_notification(
            self,
            notification: NotificationT
        ) -> None:
            """Send one-way notification that doesn't expect response."""
            # Notification handling implementation

        async def _received_request(
            self,
            responder: RequestResponder[ReceiveRequestT, ResultT]
        ) -> None:
            """Handle incoming request from other side."""
            # Request handling implementation

        async def _received_notification(
            self,
            notification: ReceiveNotificationT
        ) -> None:
            """Handle incoming notification from other side."""
            # Notification handling implementation
    ```
  </Tab>
</Tabs>

Key classes include:

* `Protocol`
* `Client`
* `Server`

### Transport layer

The transport layer handles the actual communication between clients and servers. MCP supports multiple transport mechanisms:

1. **Stdio transport**

   * Uses standard input/output for communication
   * Ideal for local processes

2. **Streamable HTTP transport**
   * Uses HTTP with optional Server-Sent Events for streaming
   * HTTP POST for client-to-server messages

All transports use [JSON-RPC](https://www.jsonrpc.org/) 2.0 to exchange messages. See the [specification](/specification/) for detailed information about the Model Context Protocol message format.

### Message types

MCP has these main types of messages:

1. **Requests** expect a response from the other side:

   ```typescript
   interface Request {
     method: string;
     params?: { ... };
   }
   ```

2. **Results** are successful responses to requests:

   ```typescript
   interface Result {
     [key: string]: unknown;
   }
   ```

3. **Errors** indicate that a request failed:

   ```typescript
   interface Error {
     code: number;
     message: string;
     data?: unknown;
   }
   ```

4. **Notifications** are one-way messages that don't expect a response:
   ```typescript
   interface Notification {
     method: string;
     params?: { ... };
   }
   ```

## Connection lifecycle

### 1. Initialization

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Client->>Server: initialize request
    Server->>Client: initialize response
    Client->>Server: initialized notification

    Note over Client,Server: Connection ready for use
```

1. Client sends `initialize` request with protocol version and capabilities
2. Server responds with its protocol version and capabilities
3. Client sends `initialized` notification as acknowledgment
4. Normal message exchange begins

### 2. Message exchange

After initialization, the following patterns are supported:

* **Request-Response**: Client or server sends requests, the other responds
* **Notifications**: Either party sends one-way messages

### 3. Termination

Either party can terminate the connection:

* Clean shutdown via `close()`
* Transport disconnection
* Error conditions

## Error handling

MCP defines these standard error codes:

```typescript
enum ErrorCode {
  // Standard JSON-RPC error codes
  ParseError = -32700,
  InvalidRequest = -32600,
  MethodNotFound = -32601,
  InvalidParams = -32602,
  InternalError = -32603,
}
```

SDKs and applications can define their own error codes above -32000.

Errors are propagated through:

* Error responses to requests
* Error events on transports
* Protocol-level error handlers

## Implementation example

Here's a basic example of implementing an MCP server:

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    import { Server } from "@modelcontextprotocol/sdk/server/index.js";
    import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

    const server = new Server({
      name: "example-server",
      version: "1.0.0"
    }, {
      capabilities: {
        resources: {}
      }
    });

    // Handle requests
    server.setRequestHandler(ListResourcesRequestSchema, async () => {
      return {
        resources: [
          {
            uri: "example://resource",
            name: "Example Resource"
          }
        ]
      };
    });

    // Connect transport
    const transport = new StdioServerTransport();
    await server.connect(transport);
    ```
  </Tab>

  <Tab title="Python">
    ```python
    import asyncio
    import mcp.types as types
    from mcp.server import Server
    from mcp.server.stdio import stdio_server

    app = Server("example-server")

    @app.list_resources()
    async def list_resources() -> list[types.Resource]:
        return [
            types.Resource(
                uri="example://resource",
                name="Example Resource"
            )
        ]

    async def main():
        async with stdio_server() as streams:
            await app.run(
                streams[0],
                streams[1],
                app.create_initialization_options()
            )

    if __name__ == "__main__":
        asyncio.run(main())
    ```
  </Tab>
</Tabs>

## Best practices

### Transport selection

1. **Local communication**

   * Use stdio transport for local processes
   * Efficient for same-machine communication
   * Simple process management

2. **Remote communication**
   * Use Streamable HTTP for scenarios requiring HTTP compatibility
   * Consider security implications including authentication and authorization

### Message handling

1. **Request processing**

   * Validate inputs thoroughly
   * Use type-safe schemas
   * Handle errors gracefully
   * Implement timeouts

2. **Progress reporting**

   * Use progress tokens for long operations
   * Report progress incrementally
   * Include total progress when known

3. **Error management**
   * Use appropriate error codes
   * Include helpful error messages
   * Clean up resources on errors

## Security considerations

1. **Transport security**

   * Use TLS for remote connections
   * Validate connection origins
   * Implement authentication when needed

2. **Message validation**

   * Validate all incoming messages
   * Sanitize inputs
   * Check message size limits
   * Verify JSON-RPC format

3. **Resource protection**

   * Implement access controls
   * Validate resource paths
   * Monitor resource usage
   * Rate limit requests

4. **Error handling**
   * Don't leak sensitive information
   * Log security-relevant errors
   * Implement proper cleanup
   * Handle DoS scenarios

## Debugging and monitoring

1. **Logging**

   * Log protocol events
   * Track message flow
   * Monitor performance
   * Record errors

2. **Diagnostics**

   * Implement health checks
   * Monitor connection state
   * Track resource usage
   * Profile performance

3. **Testing**
   * Test different transports
   * Verify error handling
   * Check edge cases
   * Load test servers


# Prompts
Source: https://modelcontextprotocol.io/docs/concepts/prompts

Create reusable prompt templates and workflows

Prompts enable servers to define reusable prompt templates and workflows that clients can easily surface to users and LLMs. They provide a powerful way to standardize and share common LLM interactions.

<Note>
  Prompts are designed to be **user-controlled**, meaning they are exposed from servers to clients with the intention of the user being able to explicitly select them for use.
</Note>

## Overview

Prompts in MCP are predefined templates that can:

* Accept dynamic arguments
* Include context from resources
* Chain multiple interactions
* Guide specific workflows
* Surface as UI elements (like slash commands)

## Prompt structure

Each prompt is defined with:

```typescript
{
  name: string;              // Unique identifier for the prompt
  description?: string;      // Human-readable description
  arguments?: [              // Optional list of arguments
    {
      name: string;          // Argument identifier
      description?: string;  // Argument description
      required?: boolean;    // Whether argument is required
    }
  ]
}
```

## Discovering prompts

Clients can discover available prompts by sending a `prompts/list` request:

```typescript
// Request
{
  method: "prompts/list";
}

// Response
{
  prompts: [
    {
      name: "analyze-code",
      description: "Analyze code for potential improvements",
      arguments: [
        {
          name: "language",
          description: "Programming language",
          required: true,
        },
      ],
    },
  ];
}
```

## Using prompts

To use a prompt, clients make a `prompts/get` request:

````typescript
// Request
{
  method: "prompts/get",
  params: {
    name: "analyze-code",
    arguments: {
      language: "python"
    }
  }
}

// Response
{
  description: "Analyze Python code for potential improvements",
  messages: [
    {
      role: "user",
      content: {
        type: "text",
        text: "Please analyze the following Python code for potential improvements:\n\n```python\ndef calculate_sum(numbers):\n    total = 0\n    for num in numbers:\n        total = total + num\n    return total\n\nresult = calculate_sum([1, 2, 3, 4, 5])\nprint(result)\n```"
      }
    }
  ]
}
````

## Dynamic prompts

Prompts can be dynamic and include:

### Embedded resource context

```json
{
  "name": "analyze-project",
  "description": "Analyze project logs and code",
  "arguments": [
    {
      "name": "timeframe",
      "description": "Time period to analyze logs",
      "required": true
    },
    {
      "name": "fileUri",
      "description": "URI of code file to review",
      "required": true
    }
  ]
}
```

When handling the `prompts/get` request:

```json
{
  "messages": [
    {
      "role": "user",
      "content": {
        "type": "text",
        "text": "Analyze these system logs and the code file for any issues:"
      }
    },
    {
      "role": "user",
      "content": {
        "type": "resource",
        "resource": {
          "uri": "logs://recent?timeframe=1h",
          "text": "[2024-03-14 15:32:11] ERROR: Connection timeout in network.py:127\n[2024-03-14 15:32:15] WARN: Retrying connection (attempt 2/3)\n[2024-03-14 15:32:20] ERROR: Max retries exceeded",
          "mimeType": "text/plain"
        }
      }
    },
    {
      "role": "user",
      "content": {
        "type": "resource",
        "resource": {
          "uri": "file:///path/to/code.py",
          "text": "def connect_to_service(timeout=30):\n    retries = 3\n    for attempt in range(retries):\n        try:\n            return establish_connection(timeout)\n        except TimeoutError:\n            if attempt == retries - 1:\n                raise\n            time.sleep(5)\n\ndef establish_connection(timeout):\n    # Connection implementation\n    pass",
          "mimeType": "text/x-python"
        }
      }
    }
  ]
}
```

### Multi-step workflows

```typescript
const debugWorkflow = {
  name: "debug-error",
  async getMessages(error: string) {
    return [
      {
        role: "user",
        content: {
          type: "text",
          text: `Here's an error I'm seeing: ${error}`,
        },
      },
      {
        role: "assistant",
        content: {
          type: "text",
          text: "I'll help analyze this error. What have you tried so far?",
        },
      },
      {
        role: "user",
        content: {
          type: "text",
          text: "I've tried restarting the service, but the error persists.",
        },
      },
    ];
  },
};
```

## Example implementation

Here's a complete example of implementing prompts in an MCP server:

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    import { Server } from "@modelcontextprotocol/sdk/server";
    import {
      ListPromptsRequestSchema,
      GetPromptRequestSchema
    } from "@modelcontextprotocol/sdk/types";

    const PROMPTS = {
      "git-commit": {
        name: "git-commit",
        description: "Generate a Git commit message",
        arguments: [
          {
            name: "changes",
            description: "Git diff or description of changes",
            required: true
          }
        ]
      },
      "explain-code": {
        name: "explain-code",
        description: "Explain how code works",
        arguments: [
          {
            name: "code",
            description: "Code to explain",
            required: true
          },
          {
            name: "language",
            description: "Programming language",
            required: false
          }
        ]
      }
    };

    const server = new Server({
      name: "example-prompts-server",
      version: "1.0.0"
    }, {
      capabilities: {
        prompts: {}
      }
    });

    // List available prompts
    server.setRequestHandler(ListPromptsRequestSchema, async () => {
      return {
        prompts: Object.values(PROMPTS)
      };
    });

    // Get specific prompt
    server.setRequestHandler(GetPromptRequestSchema, async (request) => {
      const prompt = PROMPTS[request.params.name];
      if (!prompt) {
        throw new Error(`Prompt not found: ${request.params.name}`);
      }

      if (request.params.name === "git-commit") {
        return {
          messages: [
            {
              role: "user",
              content: {
                type: "text",
                text: `Generate a concise but descriptive commit message for these changes:\n\n${request.params.arguments?.changes}`
              }
            }
          ]
        };
      }

      if (request.params.name === "explain-code") {
        const language = request.params.arguments?.language || "Unknown";
        return {
          messages: [
            {
              role: "user",
              content: {
                type: "text",
                text: `Explain how this ${language} code works:\n\n${request.params.arguments?.code}`
              }
            }
          ]
        };
      }

      throw new Error("Prompt implementation not found");
    });
    ```
  </Tab>

  <Tab title="Python">
    ```python
    from mcp.server import Server
    import mcp.types as types

    # Define available prompts
    PROMPTS = {
        "git-commit": types.Prompt(
            name="git-commit",
            description="Generate a Git commit message",
            arguments=[
                types.PromptArgument(
                    name="changes",
                    description="Git diff or description of changes",
                    required=True
                )
            ],
        ),
        "explain-code": types.Prompt(
            name="explain-code",
            description="Explain how code works",
            arguments=[
                types.PromptArgument(
                    name="code",
                    description="Code to explain",
                    required=True
                ),
                types.PromptArgument(
                    name="language",
                    description="Programming language",
                    required=False
                )
            ],
        )
    }

    # Initialize server
    app = Server("example-prompts-server")

    @app.list_prompts()
    async def list_prompts() -> list[types.Prompt]:
        return list(PROMPTS.values())

    @app.get_prompt()
    async def get_prompt(
        name: str, arguments: dict[str, str] | None = None
    ) -> types.GetPromptResult:
        if name not in PROMPTS:
            raise ValueError(f"Prompt not found: {name}")

        if name == "git-commit":
            changes = arguments.get("changes") if arguments else ""
            return types.GetPromptResult(
                messages=[
                    types.PromptMessage(
                        role="user",
                        content=types.TextContent(
                            type="text",
                            text=f"Generate a concise but descriptive commit message "
                            f"for these changes:\n\n{changes}"
                        )
                    )
                ]
            )

        if name == "explain-code":
            code = arguments.get("code") if arguments else ""
            language = arguments.get("language", "Unknown") if arguments else "Unknown"
            return types.GetPromptResult(
                messages=[
                    types.PromptMessage(
                        role="user",
                        content=types.TextContent(
                            type="text",
                            text=f"Explain how this {language} code works:\n\n{code}"
                        )
                    )
                ]
            )

        raise ValueError("Prompt implementation not found")
    ```
  </Tab>
</Tabs>

## Best practices

When implementing prompts:

1. Use clear, descriptive prompt names
2. Provide detailed descriptions for prompts and arguments
3. Validate all required arguments
4. Handle missing arguments gracefully
5. Consider versioning for prompt templates
6. Cache dynamic content when appropriate
7. Implement error handling
8. Document expected argument formats
9. Consider prompt composability
10. Test prompts with various inputs

## UI integration

Prompts can be surfaced in client UIs as:

* Slash commands
* Quick actions
* Context menu items
* Command palette entries
* Guided workflows
* Interactive forms

## Updates and changes

Servers can notify clients about prompt changes:

1. Server capability: `prompts.listChanged`
2. Notification: `notifications/prompts/list_changed`
3. Client re-fetches prompt list

## Security considerations

When implementing prompts:

* Validate all arguments
* Sanitize user input
* Consider rate limiting
* Implement access controls
* Audit prompt usage
* Handle sensitive data appropriately
* Validate generated content
* Implement timeouts
* Consider prompt injection risks
* Document security requirements


# Resources
Source: https://modelcontextprotocol.io/docs/concepts/resources

Expose data and content from your servers to LLMs

Resources are a core primitive in the Model Context Protocol (MCP) that allow servers to expose data and content that can be read by clients and used as context for LLM interactions.

<Note>
  Resources are designed to be **application-controlled**, meaning that the client application can decide how and when they should be used.
  Different MCP clients may handle resources differently. For example:

  * Claude Desktop currently requires users to explicitly select resources before they can be used
  * Other clients might automatically select resources based on heuristics
  * Some implementations may even allow the AI model itself to determine which resources to use

  Server authors should be prepared to handle any of these interaction patterns when implementing resource support. In order to expose data to models automatically, server authors should use a **model-controlled** primitive such as [Tools](./tools).
</Note>

## Overview

Resources represent any kind of data that an MCP server wants to make available to clients. This can include:

* File contents
* Database records
* API responses
* Live system data
* Screenshots and images
* Log files
* And more

Each resource is identified by a unique URI and can contain either text or binary data.

## Resource URIs

Resources are identified using URIs that follow this format:

```
[protocol]://[host]/[path]
```

For example:

* `file:///home/user/documents/report.pdf`
* `postgres://database/customers/schema`
* `screen://localhost/display1`

The protocol and path structure is defined by the MCP server implementation. Servers can define their own custom URI schemes.

## Resource types

Resources can contain two types of content:

### Text resources

Text resources contain UTF-8 encoded text data. These are suitable for:

* Source code
* Configuration files
* Log files
* JSON/XML data
* Plain text

### Binary resources

Binary resources contain raw binary data encoded in base64. These are suitable for:

* Images
* PDFs
* Audio files
* Video files
* Other non-text formats

## Resource discovery

Clients can discover available resources through two main methods:

### Direct resources

Servers expose a list of resources via the `resources/list` request. Each resource includes:

```typescript
{
  uri: string;           // Unique identifier for the resource
  name: string;          // Human-readable name
  description?: string;  // Optional description
  mimeType?: string;     // Optional MIME type
  size?: number;         // Optional size in bytes
}
```

### Resource templates

For dynamic resources, servers can expose [URI templates](https://datatracker.ietf.org/doc/html/rfc6570) that clients can use to construct valid resource URIs:

```typescript
{
  uriTemplate: string;   // URI template following RFC 6570
  name: string;          // Human-readable name for this type
  description?: string;  // Optional description
  mimeType?: string;     // Optional MIME type for all matching resources
}
```

## Reading resources

To read a resource, clients make a `resources/read` request with the resource URI.

The server responds with a list of resource contents:

```typescript
{
  contents: [
    {
      uri: string;        // The URI of the resource
      mimeType?: string;  // Optional MIME type

      // One of:
      text?: string;      // For text resources
      blob?: string;      // For binary resources (base64 encoded)
    }
  ]
}
```

<Tip>
  Servers may return multiple resources in response to one `resources/read` request. This could be used, for example, to return a list of files inside a directory when the directory is read.
</Tip>

## Resource updates

MCP supports real-time updates for resources through two mechanisms:

### List changes

Servers can notify clients when their list of available resources changes via the `notifications/resources/list_changed` notification.

### Content changes

Clients can subscribe to updates for specific resources:

1. Client sends `resources/subscribe` with resource URI
2. Server sends `notifications/resources/updated` when the resource changes
3. Client can fetch latest content with `resources/read`
4. Client can unsubscribe with `resources/unsubscribe`

## Example implementation

Here's a simple example of implementing resource support in an MCP server:

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    const server = new Server({
      name: "example-server",
      version: "1.0.0"
    }, {
      capabilities: {
        resources: {}
      }
    });

    // List available resources
    server.setRequestHandler(ListResourcesRequestSchema, async () => {
      return {
        resources: [
          {
            uri: "file:///logs/app.log",
            name: "Application Logs",
            mimeType: "text/plain"
          }
        ]
      };
    });

    // Read resource contents
    server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
      const uri = request.params.uri;

      if (uri === "file:///logs/app.log") {
        const logContents = await readLogFile();
        return {
          contents: [
            {
              uri,
              mimeType: "text/plain",
              text: logContents
            }
          ]
        };
      }

      throw new Error("Resource not found");
    });
    ```
  </Tab>

  <Tab title="Python">
    ```python
    app = Server("example-server")

    @app.list_resources()
    async def list_resources() -> list[types.Resource]:
        return [
            types.Resource(
                uri="file:///logs/app.log",
                name="Application Logs",
                mimeType="text/plain"
            )
        ]

    @app.read_resource()
    async def read_resource(uri: AnyUrl) -> str:
        if str(uri) == "file:///logs/app.log":
            log_contents = await read_log_file()
            return log_contents

        raise ValueError("Resource not found")

    # Start server
    async with stdio_server() as streams:
        await app.run(
            streams[0],
            streams[1],
            app.create_initialization_options()
        )
    ```
  </Tab>
</Tabs>

## Best practices

When implementing resource support:

1. Use clear, descriptive resource names and URIs
2. Include helpful descriptions to guide LLM understanding
3. Set appropriate MIME types when known
4. Implement resource templates for dynamic content
5. Use subscriptions for frequently changing resources
6. Handle errors gracefully with clear error messages
7. Consider pagination for large resource lists
8. Cache resource contents when appropriate
9. Validate URIs before processing
10. Document your custom URI schemes

## Security considerations

When exposing resources:

* Validate all resource URIs
* Implement appropriate access controls
* Sanitize file paths to prevent directory traversal
* Be cautious with binary data handling
* Consider rate limiting for resource reads
* Audit resource access
* Encrypt sensitive data in transit
* Validate MIME types
* Implement timeouts for long-running reads
* Handle resource cleanup appropriately


# Roots
Source: https://modelcontextprotocol.io/docs/concepts/roots

Understanding roots in MCP

Roots are a concept in MCP that define the boundaries where servers can operate. They provide a way for clients to inform servers about relevant resources and their locations.

## What are Roots?

A root is a URI that a client suggests a server should focus on. When a client connects to a server, it declares which roots the server should work with. While primarily used for filesystem paths, roots can be any valid URI including HTTP URLs.

For example, roots could be:

```
file:///home/user/projects/myapp
https://api.example.com/v1
```

## Why Use Roots?

Roots serve several important purposes:

1. **Guidance**: They inform servers about relevant resources and locations
2. **Clarity**: Roots make it clear which resources are part of your workspace
3. **Organization**: Multiple roots let you work with different resources simultaneously

## How Roots Work

When a client supports roots, it:

1. Declares the `roots` capability during connection
2. Provides a list of suggested roots to the server
3. Notifies the server when roots change (if supported)

While roots are informational and not strictly enforcing, servers should:

1. Respect the provided roots
2. Use root URIs to locate and access resources
3. Prioritize operations within root boundaries

## Common Use Cases

Roots are commonly used to define:

* Project directories
* Repository locations
* API endpoints
* Configuration locations
* Resource boundaries

## Best Practices

When working with roots:

1. Only suggest necessary resources
2. Use clear, descriptive names for roots
3. Monitor root accessibility
4. Handle root changes gracefully

## Example

Here's how a typical MCP client might expose roots:

```json
{
  "roots": [
    {
      "uri": "file:///home/user/projects/frontend",
      "name": "Frontend Repository"
    },
    {
      "uri": "https://api.example.com/v1",
      "name": "API Endpoint"
    }
  ]
}
```

This configuration suggests the server focus on both a local repository and an API endpoint while keeping them logically separated.


# Sampling
Source: https://modelcontextprotocol.io/docs/concepts/sampling

Let your servers request completions from LLMs

Sampling is a powerful MCP feature that allows servers to request LLM completions through the client, enabling sophisticated agentic behaviors while maintaining security and privacy.

<Info>
  This feature of MCP is not yet supported in the Claude Desktop client.
</Info>

## How sampling works

The sampling flow follows these steps:

1. Server sends a `sampling/createMessage` request to the client
2. Client reviews the request and can modify it
3. Client samples from an LLM
4. Client reviews the completion
5. Client returns the result to the server

This human-in-the-loop design ensures users maintain control over what the LLM sees and generates.

## Message format

Sampling requests use a standardized message format:

```typescript
{
  messages: [
    {
      role: "user" | "assistant",
      content: {
        type: "text" | "image",

        // For text:
        text?: string,

        // For images:
        data?: string,             // base64 encoded
        mimeType?: string
      }
    }
  ],
  modelPreferences?: {
    hints?: [{
      name?: string                // Suggested model name/family
    }],
    costPriority?: number,         // 0-1, importance of minimizing cost
    speedPriority?: number,        // 0-1, importance of low latency
    intelligencePriority?: number  // 0-1, importance of capabilities
  },
  systemPrompt?: string,
  includeContext?: "none" | "thisServer" | "allServers",
  temperature?: number,
  maxTokens: number,
  stopSequences?: string[],
  metadata?: Record<string, unknown>
}
```

## Request parameters

### Messages

The `messages` array contains the conversation history to send to the LLM. Each message has:

* `role`: Either "user" or "assistant"
* `content`: The message content, which can be:
  * Text content with a `text` field
  * Image content with `data` (base64) and `mimeType` fields

### Model preferences

The `modelPreferences` object allows servers to specify their model selection preferences:

* `hints`: Array of model name suggestions that clients can use to select an appropriate model:

  * `name`: String that can match full or partial model names (e.g. "claude-3", "sonnet")
  * Clients may map hints to equivalent models from different providers
  * Multiple hints are evaluated in preference order

* Priority values (0-1 normalized):
  * `costPriority`: Importance of minimizing costs
  * `speedPriority`: Importance of low latency response
  * `intelligencePriority`: Importance of advanced model capabilities

Clients make the final model selection based on these preferences and their available models.

### System prompt

An optional `systemPrompt` field allows servers to request a specific system prompt. The client may modify or ignore this.

### Context inclusion

The `includeContext` parameter specifies what MCP context to include:

* `"none"`: No additional context
* `"thisServer"`: Include context from the requesting server
* `"allServers"`: Include context from all connected MCP servers

The client controls what context is actually included.

### Sampling parameters

Fine-tune the LLM sampling with:

* `temperature`: Controls randomness (0.0 to 1.0)
* `maxTokens`: Maximum tokens to generate
* `stopSequences`: Array of sequences that stop generation
* `metadata`: Additional provider-specific parameters

## Response format

The client returns a completion result:

```typescript
{
  model: string,  // Name of the model used
  stopReason?: "endTurn" | "stopSequence" | "maxTokens" | string,
  role: "user" | "assistant",
  content: {
    type: "text" | "image",
    text?: string,
    data?: string,
    mimeType?: string
  }
}
```

## Example request

Here's an example of requesting sampling from a client:

```json
{
  "method": "sampling/createMessage",
  "params": {
    "messages": [
      {
        "role": "user",
        "content": {
          "type": "text",
          "text": "What files are in the current directory?"
        }
      }
    ],
    "systemPrompt": "You are a helpful file system assistant.",
    "includeContext": "thisServer",
    "maxTokens": 100
  }
}
```

## Best practices

When implementing sampling:

1. Always provide clear, well-structured prompts
2. Handle both text and image content appropriately
3. Set reasonable token limits
4. Include relevant context through `includeContext`
5. Validate responses before using them
6. Handle errors gracefully
7. Consider rate limiting sampling requests
8. Document expected sampling behavior
9. Test with various model parameters
10. Monitor sampling costs

## Human in the loop controls

Sampling is designed with human oversight in mind:

### For prompts

* Clients should show users the proposed prompt
* Users should be able to modify or reject prompts
* System prompts can be filtered or modified
* Context inclusion is controlled by the client

### For completions

* Clients should show users the completion
* Users should be able to modify or reject completions
* Clients can filter or modify completions
* Users control which model is used

## Security considerations

When implementing sampling:

* Validate all message content
* Sanitize sensitive information
* Implement appropriate rate limits
* Monitor sampling usage
* Encrypt data in transit
* Handle user data privacy
* Audit sampling requests
* Control cost exposure
* Implement timeouts
* Handle model errors gracefully

## Common patterns

### Agentic workflows

Sampling enables agentic patterns like:

* Reading and analyzing resources
* Making decisions based on context
* Generating structured data
* Handling multi-step tasks
* Providing interactive assistance

### Context management

Best practices for context:

* Request minimal necessary context
* Structure context clearly
* Handle context size limits
* Update context as needed
* Clean up stale context

### Error handling

Robust error handling should:

* Catch sampling failures
* Handle timeout errors
* Manage rate limits
* Validate responses
* Provide fallback behaviors
* Log errors appropriately

## Limitations

Be aware of these limitations:

* Sampling depends on client capabilities
* Users control sampling behavior
* Context size has limits
* Rate limits may apply
* Costs should be considered
* Model availability varies
* Response times vary
* Not all content types supported


# Tools
Source: https://modelcontextprotocol.io/docs/concepts/tools

Enable LLMs to perform actions through your server

Tools are a powerful primitive in the Model Context Protocol (MCP) that enable servers to expose executable functionality to clients. Through tools, LLMs can interact with external systems, perform computations, and take actions in the real world.

<Note>
  Tools are designed to be **model-controlled**, meaning that tools are exposed from servers to clients with the intention of the AI model being able to automatically invoke them (with a human in the loop to grant approval).
</Note>

## Overview

Tools in MCP allow servers to expose executable functions that can be invoked by clients and used by LLMs to perform actions. Key aspects of tools include:

* **Discovery**: Clients can obtain a list of available tools by sending a `tools/list` request
* **Invocation**: Tools are called using the `tools/call` request, where servers perform the requested operation and return results
* **Flexibility**: Tools can range from simple calculations to complex API interactions

Like [resources](/docs/concepts/resources), tools are identified by unique names and can include descriptions to guide their usage. However, unlike resources, tools represent dynamic operations that can modify state or interact with external systems.

## Tool definition structure

Each tool is defined with the following structure:

```typescript
{
  name: string;          // Unique identifier for the tool
  description?: string;  // Human-readable description
  inputSchema: {         // JSON Schema for the tool's parameters
    type: "object",
    properties: { ... }  // Tool-specific parameters
  },
  annotations?: {        // Optional hints about tool behavior
    title?: string;      // Human-readable title for the tool
    readOnlyHint?: boolean;    // If true, the tool does not modify its environment
    destructiveHint?: boolean; // If true, the tool may perform destructive updates
    idempotentHint?: boolean;  // If true, repeated calls with same args have no additional effect
    openWorldHint?: boolean;   // If true, tool interacts with external entities
  }
}
```

## Implementing tools

Here's an example of implementing a basic tool in an MCP server:

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    const server = new Server({
      name: "example-server",
      version: "1.0.0"
    }, {
      capabilities: {
        tools: {}
      }
    });

    // Define available tools
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: [{
          name: "calculate_sum",
          description: "Add two numbers together",
          inputSchema: {
            type: "object",
            properties: {
              a: { type: "number" },
              b: { type: "number" }
            },
            required: ["a", "b"]
          }
        }]
      };
    });

    // Handle tool execution
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      if (request.params.name === "calculate_sum") {
        const { a, b } = request.params.arguments;
        return {
          content: [
            {
              type: "text",
              text: String(a + b)
            }
          ]
        };
      }
      throw new Error("Tool not found");
    });
    ```
  </Tab>

  <Tab title="Python">
    ```python
    app = Server("example-server")

    @app.list_tools()
    async def list_tools() -> list[types.Tool]:
        return [
            types.Tool(
                name="calculate_sum",
                description="Add two numbers together",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "a": {"type": "number"},
                        "b": {"type": "number"}
                    },
                    "required": ["a", "b"]
                }
            )
        ]

    @app.call_tool()
    async def call_tool(
        name: str,
        arguments: dict
    ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
        if name == "calculate_sum":
            a = arguments["a"]
            b = arguments["b"]
            result = a + b
            return [types.TextContent(type="text", text=str(result))]
        raise ValueError(f"Tool not found: {name}")
    ```
  </Tab>
</Tabs>

## Example tool patterns

Here are some examples of types of tools that a server could provide:

### System operations

Tools that interact with the local system:

```typescript
{
  name: "execute_command",
  description: "Run a shell command",
  inputSchema: {
    type: "object",
    properties: {
      command: { type: "string" },
      args: { type: "array", items: { type: "string" } }
    }
  }
}
```

### API integrations

Tools that wrap external APIs:

```typescript
{
  name: "github_create_issue",
  description: "Create a GitHub issue",
  inputSchema: {
    type: "object",
    properties: {
      title: { type: "string" },
      body: { type: "string" },
      labels: { type: "array", items: { type: "string" } }
    }
  }
}
```

### Data processing

Tools that transform or analyze data:

```typescript
{
  name: "analyze_csv",
  description: "Analyze a CSV file",
  inputSchema: {
    type: "object",
    properties: {
      filepath: { type: "string" },
      operations: {
        type: "array",
        items: {
          enum: ["sum", "average", "count"]
        }
      }
    }
  }
}
```

## Best practices

When implementing tools:

1. Provide clear, descriptive names and descriptions
2. Use detailed JSON Schema definitions for parameters
3. Include examples in tool descriptions to demonstrate how the model should use them
4. Implement proper error handling and validation
5. Use progress reporting for long operations
6. Keep tool operations focused and atomic
7. Document expected return value structures
8. Implement proper timeouts
9. Consider rate limiting for resource-intensive operations
10. Log tool usage for debugging and monitoring

## Security considerations

When exposing tools:

### Input validation

* Validate all parameters against the schema
* Sanitize file paths and system commands
* Validate URLs and external identifiers
* Check parameter sizes and ranges
* Prevent command injection

### Access control

* Implement authentication where needed
* Use appropriate authorization checks
* Audit tool usage
* Rate limit requests
* Monitor for abuse

### Error handling

* Don't expose internal errors to clients
* Log security-relevant errors
* Handle timeouts appropriately
* Clean up resources after errors
* Validate return values

## Tool discovery and updates

MCP supports dynamic tool discovery:

1. Clients can list available tools at any time
2. Servers can notify clients when tools change using `notifications/tools/list_changed`
3. Tools can be added or removed during runtime
4. Tool definitions can be updated (though this should be done carefully)

## Error handling

Tool errors should be reported within the result object, not as MCP protocol-level errors. This allows the LLM to see and potentially handle the error. When a tool encounters an error:

1. Set `isError` to `true` in the result
2. Include error details in the `content` array

Here's an example of proper error handling for tools:

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    try {
      // Tool operation
      const result = performOperation();
      return {
        content: [
          {
            type: "text",
            text: `Operation successful: ${result}`
          }
        ]
      };
    } catch (error) {
      return {
        isError: true,
        content: [
          {
            type: "text",
            text: `Error: ${error.message}`
          }
        ]
      };
    }
    ```
  </Tab>

  <Tab title="Python">
    ```python
    try:
        # Tool operation
        result = perform_operation()
        return types.CallToolResult(
            content=[
                types.TextContent(
                    type="text",
                    text=f"Operation successful: {result}"
                )
            ]
        )
    except Exception as error:
        return types.CallToolResult(
            isError=True,
            content=[
                types.TextContent(
                    type="text",
                    text=f"Error: {str(error)}"
                )
            ]
        )
    ```
  </Tab>
</Tabs>

This approach allows the LLM to see that an error occurred and potentially take corrective action or request human intervention.

## Tool annotations

Tool annotations provide additional metadata about a tool's behavior, helping clients understand how to present and manage tools. These annotations are hints that describe the nature and impact of a tool, but should not be relied upon for security decisions.

### Purpose of tool annotations

Tool annotations serve several key purposes:

1. Provide UX-specific information without affecting model context
2. Help clients categorize and present tools appropriately
3. Convey information about a tool's potential side effects
4. Assist in developing intuitive interfaces for tool approval

### Available tool annotations

The MCP specification defines the following annotations for tools:

| Annotation        | Type    | Default | Description                                                                                                                          |
| ----------------- | ------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| `title`           | string  | -       | A human-readable title for the tool, useful for UI display                                                                           |
| `readOnlyHint`    | boolean | false   | If true, indicates the tool does not modify its environment                                                                          |
| `destructiveHint` | boolean | true    | If true, the tool may perform destructive updates (only meaningful when `readOnlyHint` is false)                                     |
| `idempotentHint`  | boolean | false   | If true, calling the tool repeatedly with the same arguments has no additional effect (only meaningful when `readOnlyHint` is false) |
| `openWorldHint`   | boolean | true    | If true, the tool may interact with an "open world" of external entities                                                             |

### Example usage

Here's how to define tools with annotations for different scenarios:

```typescript
// A read-only search tool
{
  name: "web_search",
  description: "Search the web for information",
  inputSchema: {
    type: "object",
    properties: {
      query: { type: "string" }
    },
    required: ["query"]
  },
  annotations: {
    title: "Web Search",
    readOnlyHint: true,
    openWorldHint: true
  }
}

// A destructive file deletion tool
{
  name: "delete_file",
  description: "Delete a file from the filesystem",
  inputSchema: {
    type: "object",
    properties: {
      path: { type: "string" }
    },
    required: ["path"]
  },
  annotations: {
    title: "Delete File",
    readOnlyHint: false,
    destructiveHint: true,
    idempotentHint: true,
    openWorldHint: false
  }
}

// A non-destructive database record creation tool
{
  name: "create_record",
  description: "Create a new record in the database",
  inputSchema: {
    type: "object",
    properties: {
      table: { type: "string" },
      data: { type: "object" }
    },
    required: ["table", "data"]
  },
  annotations: {
    title: "Create Database Record",
    readOnlyHint: false,
    destructiveHint: false,
    idempotentHint: false,
    openWorldHint: false
  }
}
```

### Integrating annotations in server implementation

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: [{
          name: "calculate_sum",
          description: "Add two numbers together",
          inputSchema: {
            type: "object",
            properties: {
              a: { type: "number" },
              b: { type: "number" }
            },
            required: ["a", "b"]
          },
          annotations: {
            title: "Calculate Sum",
            readOnlyHint: true,
            openWorldHint: false
          }
        }]
      };
    });
    ```
  </Tab>

  <Tab title="Python">
    ```python
    from mcp.server.fastmcp import FastMCP

    mcp = FastMCP("example-server")

    @mcp.tool(
        annotations={
            "title": "Calculate Sum",
            "readOnlyHint": True,
            "openWorldHint": False
        }
    )
    async def calculate_sum(a: float, b: float) -> str:
        """Add two numbers together.

        Args:
            a: First number to add
            b: Second number to add
        """
        result = a + b
        return str(result)
    ```
  </Tab>
</Tabs>

### Best practices for tool annotations

1. **Be accurate about side effects**: Clearly indicate whether a tool modifies its environment and whether those modifications are destructive.

2. **Use descriptive titles**: Provide human-friendly titles that clearly describe the tool's purpose.

3. **Indicate idempotency properly**: Mark tools as idempotent only if repeated calls with the same arguments truly have no additional effect.

4. **Set appropriate open/closed world hints**: Indicate whether a tool interacts with a closed system (like a database) or an open system (like the web).

5. **Remember annotations are hints**: All properties in ToolAnnotations are hints and not guaranteed to provide a faithful description of tool behavior. Clients should never make security-critical decisions based solely on annotations.

## Testing tools

A comprehensive testing strategy for MCP tools should cover:

* **Functional testing**: Verify tools execute correctly with valid inputs and handle invalid inputs appropriately
* **Integration testing**: Test tool interaction with external systems using both real and mocked dependencies
* **Security testing**: Validate authentication, authorization, input sanitization, and rate limiting
* **Performance testing**: Check behavior under load, timeout handling, and resource cleanup
* **Error handling**: Ensure tools properly report errors through the MCP protocol and clean up resources


# Transports
Source: https://modelcontextprotocol.io/docs/concepts/transports

Learn about MCP's communication mechanisms

Transports in the Model Context Protocol (MCP) provide the foundation for communication between clients and servers. A transport handles the underlying mechanics of how messages are sent and received.

## Message Format

MCP uses [JSON-RPC](https://www.jsonrpc.org/) 2.0 as its wire format. The transport layer is responsible for converting MCP protocol messages into JSON-RPC format for transmission and converting received JSON-RPC messages back into MCP protocol messages.

There are three types of JSON-RPC messages used:

### Requests

```typescript
{
  jsonrpc: "2.0",
  id: number | string,
  method: string,
  params?: object
}
```

### Responses

```typescript
{
  jsonrpc: "2.0",
  id: number | string,
  result?: object,
  error?: {
    code: number,
    message: string,
    data?: unknown
  }
}
```

### Notifications

```typescript
{
  jsonrpc: "2.0",
  method: string,
  params?: object
}
```

## Built-in Transport Types

MCP currently defines two standard transport mechanisms:

### Standard Input/Output (stdio)

The stdio transport enables communication through standard input and output streams. This is particularly useful for local integrations and command-line tools.

Use stdio when:

* Building command-line tools
* Implementing local integrations
* Needing simple process communication
* Working with shell scripts

<Tabs>
  <Tab title="TypeScript (Server)">
    ```typescript
    const server = new Server({
      name: "example-server",
      version: "1.0.0"
    }, {
      capabilities: {}
    });

    const transport = new StdioServerTransport();
    await server.connect(transport);
    ```
  </Tab>

  <Tab title="TypeScript (Client)">
    ```typescript
    const client = new Client({
      name: "example-client",
      version: "1.0.0"
    }, {
      capabilities: {}
    });

    const transport = new StdioClientTransport({
      command: "./server",
      args: ["--option", "value"]
    });
    await client.connect(transport);
    ```
  </Tab>

  <Tab title="Python (Server)">
    ```python
    app = Server("example-server")

    async with stdio_server() as streams:
        await app.run(
            streams[0],
            streams[1],
            app.create_initialization_options()
        )
    ```
  </Tab>

  <Tab title="Python (Client)">
    ```python
    params = StdioServerParameters(
        command="./server",
        args=["--option", "value"]
    )

    async with stdio_client(params) as streams:
        async with ClientSession(streams[0], streams[1]) as session:
            await session.initialize()
    ```
  </Tab>
</Tabs>

### Streamable HTTP

The Streamable HTTP transport uses HTTP POST requests for client-to-server communication and optional Server-Sent Events (SSE) streams for server-to-client communication.

Use Streamable HTTP when:

* Building web-based integrations
* Needing client-server communication over HTTP
* Requiring stateful sessions
* Supporting multiple concurrent clients
* Implementing resumable connections

#### How it Works

1. **Client-to-Server Communication**: Every JSON-RPC message from client to server is sent as a new HTTP POST request to the MCP endpoint
2. **Server Responses**: The server can respond either with:
   * A single JSON response (`Content-Type: application/json`)
   * An SSE stream (`Content-Type: text/event-stream`) for multiple messages
3. **Server-to-Client Communication**: Servers can send requests/notifications to clients via:
   * SSE streams initiated by client requests
   * SSE streams from HTTP GET requests to the MCP endpoint

<Tabs>
  <Tab title="TypeScript (Server)">
    ```typescript
    import express from "express";

    const app = express();

    const server = new Server({
      name: "example-server",
      version: "1.0.0"
    }, {
      capabilities: {}
    });

    // MCP endpoint handles both POST and GET
    app.post("/mcp", async (req, res) => {
      // Handle JSON-RPC request
      const response = await server.handleRequest(req.body);

      // Return single response or SSE stream
      if (needsStreaming) {
        res.setHeader("Content-Type", "text/event-stream");
        // Send SSE events...
      } else {
        res.json(response);
      }
    });

    app.get("/mcp", (req, res) => {
      // Optional: Support server-initiated SSE streams
      res.setHeader("Content-Type", "text/event-stream");
      // Send server notifications/requests...
    });

    app.listen(3000);
    ```
  </Tab>

  <Tab title="TypeScript (Client)">
    ```typescript
    const client = new Client({
      name: "example-client",
      version: "1.0.0"
    }, {
      capabilities: {}
    });

    const transport = new HttpClientTransport(
      new URL("http://localhost:3000/mcp")
    );
    await client.connect(transport);
    ```
  </Tab>

  <Tab title="Python (Server)">
    ```python
    from mcp.server.http import HttpServerTransport
    from starlette.applications import Starlette
    from starlette.routing import Route

    app = Server("example-server")

    async def handle_mcp(scope, receive, send):
        if scope["method"] == "POST":
            # Handle JSON-RPC request
            response = await app.handle_request(request_body)

            if needs_streaming:
                # Return SSE stream
                await send_sse_response(send, response)
            else:
                # Return JSON response
                await send_json_response(send, response)

        elif scope["method"] == "GET":
            # Optional: Support server-initiated SSE streams
            await send_sse_stream(send)

    starlette_app = Starlette(
        routes=[
            Route("/mcp", endpoint=handle_mcp, methods=["POST", "GET"]),
        ]
    )
    ```
  </Tab>

  <Tab title="Python (Client)">
    ```python
    async with http_client("http://localhost:8000/mcp") as transport:
        async with ClientSession(transport[0], transport[1]) as session:
            await session.initialize()
    ```
  </Tab>
</Tabs>

#### Session Management

Streamable HTTP supports stateful sessions to maintain context across multiple requests:

1. **Session Initialization**: Servers may assign a session ID during initialization by including it in an `Mcp-Session-Id` header
2. **Session Persistence**: Clients must include the session ID in all subsequent requests using the `Mcp-Session-Id` header
3. **Session Termination**: Sessions can be explicitly terminated by sending an HTTP DELETE request with the session ID

Example session flow:

```typescript
// Server assigns session ID during initialization
app.post("/mcp", (req, res) => {
  if (req.body.method === "initialize") {
    const sessionId = generateSecureId();
    res.setHeader("Mcp-Session-Id", sessionId);
    // Store session state...
  }
  // Handle request...
});

// Client includes session ID in subsequent requests
fetch("/mcp", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "Mcp-Session-Id": sessionId,
  },
  body: JSON.stringify(request),
});
```

#### Resumability and Redelivery

To support resuming broken connections, Streamable HTTP provides:

1. **Event IDs**: Servers can attach unique IDs to SSE events for tracking
2. **Resume from Last Event**: Clients can resume by sending the `Last-Event-ID` header
3. **Message Replay**: Servers can replay missed messages from the disconnection point

This ensures reliable message delivery even with unstable network connections.

#### Security Considerations

When implementing Streamable HTTP transport, follow these security best practices:

1. **Validate Origin Headers**: Always validate the `Origin` header on all incoming connections to prevent DNS rebinding attacks
2. **Bind to Localhost**: When running locally, bind only to localhost (127.0.0.1) rather than all network interfaces (0.0.0.0)
3. **Implement Authentication**: Use proper authentication for all connections
4. **Use HTTPS**: Always use TLS/HTTPS for production deployments
5. **Validate Session IDs**: Ensure session IDs are cryptographically secure and properly validated

Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.

### Server-Sent Events (SSE) - Deprecated

<Note>
  SSE as a standalone transport is deprecated as of protocol version 2024-11-05.
  It has been replaced by Streamable HTTP, which incorporates SSE as an optional
  streaming mechanism. For backwards compatibility information, see the
  [backwards compatibility](#backwards-compatibility) section below.
</Note>

The legacy SSE transport enabled server-to-client streaming with HTTP POST requests for client-to-server communication.

Previously used when:

* Only server-to-client streaming is needed
* Working with restricted networks
* Implementing simple updates

#### Legacy Security Considerations

The deprecated SSE transport had similar security considerations to Streamable HTTP, particularly regarding DNS rebinding attacks. These same protections should be applied when using SSE streams within the Streamable HTTP transport.

<Tabs>
  <Tab title="TypeScript (Server)">
    ```typescript
    import express from "express";

    const app = express();

    const server = new Server({
      name: "example-server",
      version: "1.0.0"
    }, {
      capabilities: {}
    });

    let transport: SSEServerTransport | null = null;

    app.get("/sse", (req, res) => {
      transport = new SSEServerTransport("/messages", res);
      server.connect(transport);
    });

    app.post("/messages", (req, res) => {
      if (transport) {
        transport.handlePostMessage(req, res);
      }
    });

    app.listen(3000);
    ```
  </Tab>

  <Tab title="TypeScript (Client)">
    ```typescript
    const client = new Client({
      name: "example-client",
      version: "1.0.0"
    }, {
      capabilities: {}
    });

    const transport = new SSEClientTransport(
      new URL("http://localhost:3000/sse")
    );
    await client.connect(transport);
    ```
  </Tab>

  <Tab title="Python (Server)">
    ```python
    from mcp.server.sse import SseServerTransport
    from starlette.applications import Starlette
    from starlette.routing import Route

    app = Server("example-server")
    sse = SseServerTransport("/messages")

    async def handle_sse(scope, receive, send):
        async with sse.connect_sse(scope, receive, send) as streams:
            await app.run(streams[0], streams[1], app.create_initialization_options())

    async def handle_messages(scope, receive, send):
        await sse.handle_post_message(scope, receive, send)

    starlette_app = Starlette(
        routes=[
            Route("/sse", endpoint=handle_sse),
            Route("/messages", endpoint=handle_messages, methods=["POST"]),
        ]
    )
    ```
  </Tab>

  <Tab title="Python (Client)">
    ```python
    async with sse_client("http://localhost:8000/sse") as streams:
        async with ClientSession(streams[0], streams[1]) as session:
            await session.initialize()
    ```
  </Tab>
</Tabs>

## Custom Transports

MCP makes it easy to implement custom transports for specific needs. Any transport implementation just needs to conform to the Transport interface:

You can implement custom transports for:

* Custom network protocols
* Specialized communication channels
* Integration with existing systems
* Performance optimization

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    interface Transport {
      // Start processing messages
      start(): Promise<void>;

      // Send a JSON-RPC message
      send(message: JSONRPCMessage): Promise<void>;

      // Close the connection
      close(): Promise<void>;

      // Callbacks
      onclose?: () => void;
      onerror?: (error: Error) => void;
      onmessage?: (message: JSONRPCMessage) => void;
    }
    ```
  </Tab>

  <Tab title="Python">
    Note that while MCP Servers are often implemented with asyncio, we recommend
    implementing low-level interfaces like transports with `anyio` for wider compatibility.

    ```python
    @contextmanager
    async def create_transport(
        read_stream: MemoryObjectReceiveStream[JSONRPCMessage | Exception],
        write_stream: MemoryObjectSendStream[JSONRPCMessage]
    ):
        """
        Transport interface for MCP.

        Args:
            read_stream: Stream to read incoming messages from
            write_stream: Stream to write outgoing messages to
        """
        async with anyio.create_task_group() as tg:
            try:
                # Start processing messages
                tg.start_soon(lambda: process_messages(read_stream))

                # Send messages
                async with write_stream:
                    yield write_stream

            except Exception as exc:
                # Handle errors
                raise exc
            finally:
                # Clean up
                tg.cancel_scope.cancel()
                await write_stream.aclose()
                await read_stream.aclose()
    ```
  </Tab>
</Tabs>

## Error Handling

Transport implementations should handle various error scenarios:

1. Connection errors
2. Message parsing errors
3. Protocol errors
4. Network timeouts
5. Resource cleanup

Example error handling:

<Tabs>
  <Tab title="TypeScript">
    ```typescript
    class ExampleTransport implements Transport {
      async start() {
        try {
          // Connection logic
        } catch (error) {
          this.onerror?.(new Error(`Failed to connect: ${error}`));
          throw error;
        }
      }

      async send(message: JSONRPCMessage) {
        try {
          // Sending logic
        } catch (error) {
          this.onerror?.(new Error(`Failed to send message: ${error}`));
          throw error;
        }
      }
    }
    ```
  </Tab>

  <Tab title="Python">
    Note that while MCP Servers are often implemented with asyncio, we recommend
    implementing low-level interfaces like transports with `anyio` for wider compatibility.

    ```python
    @contextmanager
    async def example_transport(scope: Scope, receive: Receive, send: Send):
        try:
            # Create streams for bidirectional communication
            read_stream_writer, read_stream = anyio.create_memory_object_stream(0)
            write_stream, write_stream_reader = anyio.create_memory_object_stream(0)

            async def message_handler():
                try:
                    async with read_stream_writer:
                        # Message handling logic
                        pass
                except Exception as exc:
                    logger.error(f"Failed to handle message: {exc}")
                    raise exc

            async with anyio.create_task_group() as tg:
                tg.start_soon(message_handler)
                try:
                    # Yield streams for communication
                    yield read_stream, write_stream
                except Exception as exc:
                    logger.error(f"Transport error: {exc}")
                    raise exc
                finally:
                    tg.cancel_scope.cancel()
                    await write_stream.aclose()
                    await read_stream.aclose()
        except Exception as exc:
            logger.error(f"Failed to initialize transport: {exc}")
            raise exc
    ```
  </Tab>
</Tabs>

## Best Practices

When implementing or using MCP transport:

1. Handle connection lifecycle properly
2. Implement proper error handling
3. Clean up resources on connection close
4. Use appropriate timeouts
5. Validate messages before sending
6. Log transport events for debugging
7. Implement reconnection logic when appropriate
8. Handle backpressure in message queues
9. Monitor connection health
10. Implement proper security measures

## Security Considerations

When implementing transport:

### Authentication and Authorization

* Implement proper authentication mechanisms
* Validate client credentials
* Use secure token handling
* Implement authorization checks

### Data Security

* Use TLS for network transport
* Encrypt sensitive data
* Validate message integrity
* Implement message size limits
* Sanitize input data

### Network Security

* Implement rate limiting
* Use appropriate timeouts
* Handle denial of service scenarios
* Monitor for unusual patterns
* Implement proper firewall rules
* For HTTP-based transports (including Streamable HTTP), validate Origin headers to prevent DNS rebinding attacks
* For local servers, bind only to localhost (127.0.0.1) instead of all interfaces (0.0.0.0)

## Debugging Transport

Tips for debugging transport issues:

1. Enable debug logging
2. Monitor message flow
3. Check connection states
4. Validate message formats
5. Test error scenarios
6. Use network analysis tools
7. Implement health checks
8. Monitor resource usage
9. Test edge cases
10. Use proper error tracking

## Backwards Compatibility

To maintain compatibility between different protocol versions:

### For Servers Supporting Older Clients

Servers wanting to support clients using the deprecated HTTP+SSE transport should:

1. Host both the old SSE and POST endpoints alongside the new MCP endpoint
2. Handle initialization requests on both endpoints
3. Maintain separate handling logic for each transport type

### For Clients Supporting Older Servers

Clients wanting to support servers using the deprecated transport should:

1. Accept server URLs that may use either transport
2. Attempt to POST an `InitializeRequest` with proper `Accept` headers:
   * If successful, use Streamable HTTP transport
   * If it fails with 4xx status, fall back to legacy SSE transport
3. Issue a GET request expecting an SSE stream with `endpoint` event for legacy servers

Example compatibility detection:

```typescript
async function detectTransport(serverUrl: string): Promise<TransportType> {
  try {
    // Try Streamable HTTP first
    const response = await fetch(serverUrl, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Accept: "application/json, text/event-stream",
      },
      body: JSON.stringify({
        jsonrpc: "2.0",
        method: "initialize",
        params: {
          /* ... */
        },
      }),
    });

    if (response.ok) {
      return "streamable-http";
    }
  } catch (error) {
    // Fall back to legacy SSE
    const sseResponse = await fetch(serverUrl, {
      method: "GET",
      headers: { Accept: "text/event-stream" },
    });

    if (sseResponse.ok) {
      return "legacy-sse";
    }
  }

  throw new Error("Unsupported transport");
}
```


# Debugging
Source: https://modelcontextprotocol.io/docs/tools/debugging

A comprehensive guide to debugging Model Context Protocol (MCP) integrations

Effective debugging is essential when developing MCP servers or integrating them with applications. This guide covers the debugging tools and approaches available in the MCP ecosystem.

<Info>
  This guide is for macOS. Guides for other platforms are coming soon.
</Info>

## Debugging tools overview

MCP provides several tools for debugging at different levels:

1. **MCP Inspector**

   * Interactive debugging interface
   * Direct server testing
   * See the [Inspector guide](/docs/tools/inspector) for details

2. **Claude Desktop Developer Tools**

   * Integration testing
   * Log collection
   * Chrome DevTools integration

3. **Server Logging**
   * Custom logging implementations
   * Error tracking
   * Performance monitoring

## Debugging in Claude Desktop

### Checking server status

The Claude.app interface provides basic server status information:

1. Click the <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/claude-desktop-mcp-plug-icon.svg" style={{display: 'inline', margin: 0, height: '1.3em'}} /> icon to view:

   * Connected servers
   * Available prompts and resources

2. Click the "Search and tools" <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/claude-desktop-mcp-slider.svg" style={{display: 'inline', margin: 0, height: '1.3em'}} /> icon to view:
   * Tools made available to the model

### Viewing logs

Review detailed MCP logs from Claude Desktop:

```bash
# Follow logs in real-time
tail -n 20 -F ~/Library/Logs/Claude/mcp*.log
```

The logs capture:

* Server connection events
* Configuration issues
* Runtime errors
* Message exchanges

### Using Chrome DevTools

Access Chrome's developer tools inside Claude Desktop to investigate client-side errors:

1. Create a `developer_settings.json` file with `allowDevTools` set to true:

```bash
echo '{"allowDevTools": true}' > ~/Library/Application\ Support/Claude/developer_settings.json
```

2. Open DevTools: `Command-Option-Shift-i`

Note: You'll see two DevTools windows:

* Main content window
* App title bar window

Use the Console panel to inspect client-side errors.

Use the Network panel to inspect:

* Message payloads
* Connection timing

## Common issues

### Working directory

When using MCP servers with Claude Desktop:

* The working directory for servers launched via `claude_desktop_config.json` may be undefined (like `/` on macOS) since Claude Desktop could be started from anywhere
* Always use absolute paths in your configuration and `.env` files to ensure reliable operation
* For testing servers directly via command line, the working directory will be where you run the command

For example in `claude_desktop_config.json`, use:

```json
{
  "command": "npx",
  "args": [
    "-y",
    "@modelcontextprotocol/server-filesystem",
    "/Users/username/data"
  ]
}
```

Instead of relative paths like `./data`

### Environment variables

MCP servers inherit only a subset of environment variables automatically, like `USER`, `HOME`, and `PATH`.

To override the default variables or provide your own, you can specify an `env` key in `claude_desktop_config.json`:

```json
{
  "myserver": {
    "command": "mcp-server-myapp",
    "env": {
      "MYAPP_API_KEY": "some_key"
    }
  }
}
```

### Server initialization

Common initialization problems:

1. **Path Issues**

   * Incorrect server executable path
   * Missing required files
   * Permission problems
   * Try using an absolute path for `command`

2. **Configuration Errors**

   * Invalid JSON syntax
   * Missing required fields
   * Type mismatches

3. **Environment Problems**
   * Missing environment variables
   * Incorrect variable values
   * Permission restrictions

### Connection problems

When servers fail to connect:

1. Check Claude Desktop logs
2. Verify server process is running
3. Test standalone with [Inspector](/docs/tools/inspector)
4. Verify protocol compatibility

## Implementing logging

### Server-side logging

When building a server that uses the local stdio [transport](/docs/concepts/transports), all messages logged to stderr (standard error) will be captured by the host application (e.g., Claude Desktop) automatically.

<Warning>
  Local MCP servers should not log messages to stdout (standard out), as this will interfere with protocol operation.
</Warning>

For all [transports](/docs/concepts/transports), you can also provide logging to the client by sending a log message notification:

<Tabs>
  <Tab title="Python">
    ```python
    server.request_context.session.send_log_message(
      level="info",
      data="Server started successfully",
    )
    ```
  </Tab>

  <Tab title="TypeScript">
    ```typescript
    server.sendLoggingMessage({
      level: "info",
      data: "Server started successfully",
    });
    ```
  </Tab>
</Tabs>

Important events to log:

* Initialization steps
* Resource access
* Tool execution
* Error conditions
* Performance metrics

### Client-side logging

In client applications:

1. Enable debug logging
2. Monitor network traffic
3. Track message exchanges
4. Record error states

## Debugging workflow

### Development cycle

1. Initial Development

   * Use [Inspector](/docs/tools/inspector) for basic testing
   * Implement core functionality
   * Add logging points

2. Integration Testing
   * Test in Claude Desktop
   * Monitor logs
   * Check error handling

### Testing changes

To test changes efficiently:

* **Configuration changes**: Restart Claude Desktop
* **Server code changes**: Use Command-R to reload
* **Quick iteration**: Use [Inspector](/docs/tools/inspector) during development

## Best practices

### Logging strategy

1. **Structured Logging**

   * Use consistent formats
   * Include context
   * Add timestamps
   * Track request IDs

2. **Error Handling**

   * Log stack traces
   * Include error context
   * Track error patterns
   * Monitor recovery

3. **Performance Tracking**
   * Log operation timing
   * Monitor resource usage
   * Track message sizes
   * Measure latency

### Security considerations

When debugging:

1. **Sensitive Data**

   * Sanitize logs
   * Protect credentials
   * Mask personal information

2. **Access Control**
   * Verify permissions
   * Check authentication
   * Monitor access patterns

## Getting help

When encountering issues:

1. **First Steps**

   * Check server logs
   * Test with [Inspector](/docs/tools/inspector)
   * Review configuration
   * Verify environment

2. **Support Channels**

   * GitHub issues
   * GitHub discussions

3. **Providing Information**
   * Log excerpts
   * Configuration files
   * Steps to reproduce
   * Environment details

## Next steps

<CardGroup cols={2}>
  <Card title="MCP Inspector" icon="magnifying-glass" href="/docs/tools/inspector">
    Learn to use the MCP Inspector
  </Card>
</CardGroup>


# Inspector
Source: https://modelcontextprotocol.io/docs/tools/inspector

In-depth guide to using the MCP Inspector for testing and debugging Model Context Protocol servers

The [MCP Inspector](https://github.com/modelcontextprotocol/inspector) is an interactive developer tool for testing and debugging MCP servers. While the [Debugging Guide](/docs/tools/debugging) covers the Inspector as part of the overall debugging toolkit, this document provides a detailed exploration of the Inspector's features and capabilities.

## Getting started

### Installation and basic usage

The Inspector runs directly through `npx` without requiring installation:

```bash
npx @modelcontextprotocol/inspector <command>
```

```bash
npx @modelcontextprotocol/inspector <command> <arg1> <arg2>
```

#### Inspecting servers from NPM or PyPi

A common way to start server packages from [NPM](https://npmjs.com) or [PyPi](https://pypi.com).

<Tabs>
  <Tab title="NPM package">
    ```bash
    npx -y @modelcontextprotocol/inspector npx <package-name> <args>
    # For example
    npx -y @modelcontextprotocol/inspector npx @modelcontextprotocol/server-filesystem /Users/username/Desktop
    ```
  </Tab>

  <Tab title="PyPi package">
    ```bash
    npx @modelcontextprotocol/inspector uvx <package-name> <args>
    # For example
    npx @modelcontextprotocol/inspector uvx mcp-server-git --repository ~/code/mcp/servers.git
    ```
  </Tab>
</Tabs>

#### Inspecting locally developed servers

To inspect servers locally developed or downloaded as a repository, the most common
way is:

<Tabs>
  <Tab title="TypeScript">
    ```bash
    npx @modelcontextprotocol/inspector node path/to/server/index.js args...
    ```
  </Tab>

  <Tab title="Python">
    ```bash
    npx @modelcontextprotocol/inspector \
      uv \
      --directory path/to/server \
      run \
      package-name \
      args...
    ```
  </Tab>
</Tabs>

Please carefully read any attached README for the most accurate instructions.

## Feature overview

<Frame caption="The MCP Inspector interface">
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/mcp-inspector.png" />
</Frame>

The Inspector provides several features for interacting with your MCP server:

### Server connection pane

* Allows selecting the [transport](/docs/concepts/transports) for connecting to the server
* For local servers, supports customizing the command-line arguments and environment

### Resources tab

* Lists all available resources
* Shows resource metadata (MIME types, descriptions)
* Allows resource content inspection
* Supports subscription testing

### Prompts tab

* Displays available prompt templates
* Shows prompt arguments and descriptions
* Enables prompt testing with custom arguments
* Previews generated messages

### Tools tab

* Lists available tools
* Shows tool schemas and descriptions
* Enables tool testing with custom inputs
* Displays tool execution results

### Notifications pane

* Presents all logs recorded from the server
* Shows notifications received from the server

## Best practices

### Development workflow

1. Start Development

   * Launch Inspector with your server
   * Verify basic connectivity
   * Check capability negotiation

2. Iterative testing

   * Make server changes
   * Rebuild the server
   * Reconnect the Inspector
   * Test affected features
   * Monitor messages

3. Test edge cases
   * Invalid inputs
   * Missing prompt arguments
   * Concurrent operations
   * Verify error handling and error responses

## Next steps

<CardGroup cols={2}>
  <Card title="Inspector Repository" icon="github" href="https://github.com/modelcontextprotocol/inspector">
    Check out the MCP Inspector source code
  </Card>

  <Card title="Debugging Guide" icon="bug" href="/docs/tools/debugging">
    Learn about broader debugging strategies
  </Card>
</CardGroup>


# Example Servers
Source: https://modelcontextprotocol.io/examples

A list of example servers and implementations

This page showcases various Model Context Protocol (MCP) servers that demonstrate the protocol's capabilities and versatility. These servers enable Large Language Models (LLMs) to securely access tools and data sources.

## Reference implementations

These official reference servers demonstrate core MCP features and SDK usage:

### Current reference servers

* **[Filesystem](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem)** - Secure file operations with configurable access controls
* **[Fetch](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch)** - Web content fetching and conversion optimized for LLM usage
* **[Memory](https://github.com/modelcontextprotocol/servers/tree/main/src/memory)** - Knowledge graph-based persistent memory system
* **[Sequential Thinking](https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking)** - Dynamic problem-solving through thought sequences

### Archived servers (historical reference)

⚠️ **Note**: The following servers have been moved to the [servers-archived repository](https://github.com/modelcontextprotocol/servers-archived) and are no longer actively maintained. They are provided for historical reference only.

#### Data and file systems

* **[PostgreSQL](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/postgres)** - Read-only database access with schema inspection capabilities
* **[SQLite](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/sqlite)** - Database interaction and business intelligence features
* **[Google Drive](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/gdrive)** - File access and search capabilities for Google Drive

#### Development tools

* **[Git](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/git)** - Tools to read, search, and manipulate Git repositories
* **[GitHub](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/github)** - Repository management, file operations, and GitHub API integration
* **[GitLab](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/gitlab)** - GitLab API integration enabling project management
* **[Sentry](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/sentry)** - Retrieving and analyzing issues from Sentry.io

#### Web and browser automation

* **[Brave Search](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/brave-search)** - Web and local search using Brave's Search API
* **[Puppeteer](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/puppeteer)** - Browser automation and web scraping capabilities

#### Productivity and communication

* **[Slack](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/slack)** - Channel management and messaging capabilities
* **[Google Maps](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/google-maps)** - Location services, directions, and place details

#### AI and specialized tools

* **[EverArt](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/everart)** - AI image generation using various models
* **[AWS KB Retrieval](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/aws-kb-retrieval-server)** - Retrieval from AWS Knowledge Base using Bedrock Agent Runtime

## Official integrations

Visit the [MCP Servers Repository (Official Integrations section)](https://github.com/modelcontextprotocol/servers?tab=readme-ov-file#%EF%B8%8F-official-integrations) for a list of MCP servers maintained by companies for their platforms.

## Community implementations

Visit the [MCP Servers Repository (Community section)](https://github.com/modelcontextprotocol/servers?tab=readme-ov-file#-community-servers) for a list of MCP servers maintained by community members.

## Getting started

### Using reference servers

TypeScript-based servers can be used directly with `npx`:

```bash
npx -y @modelcontextprotocol/server-memory
```

Python-based servers can be used with `uvx` (recommended) or `pip`:

```bash
# Using uvx
uvx mcp-server-git

# Using pip
pip install mcp-server-git
python -m mcp_server_git
```

### Configuring with Claude

To use an MCP server with Claude, add it to your configuration:

```json
{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    },
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/path/to/allowed/files"
      ]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "<YOUR_TOKEN>"
      }
    }
  }
}
```

## Additional resources

Visit the [MCP Servers Repository (Resources section)](https://github.com/modelcontextprotocol/servers?tab=readme-ov-file#-resources) for a collection of other resources and projects related to MCP.

Visit our [GitHub Discussions](https://github.com/orgs/modelcontextprotocol/discussions) to engage with the MCP community.


# FAQs
Source: https://modelcontextprotocol.io/faqs

Explaining MCP and why it matters in simple terms

## What is MCP?

MCP (Model Context Protocol) is a standard way for AI applications and agents to connect to and work with your data sources (e.g. local files, databases, or content repositories) and tools (e.g. GitHub, Google Maps, or Puppeteer).

Think of MCP as a universal adapter for AI applications, similar to what USB-C is for physical devices. USB-C acts as a universal adapter to connect devices to various peripherals and accessories. Similarly, MCP provides a standardized way to connect AI applications to different data and tools.

Before USB-C, you needed different cables for different connections. Similarly, before MCP, developers had to build custom connections to each data source or tool they wanted their AI application to work with—a time-consuming process that often resulted in limited functionality. Now, with MCP, developers can easily add connections to their AI applications, making their applications much more powerful from day one.

## Why does MCP matter?

### For AI application users

MCP means your AI applications can access the information and tools you work with every day, making them much more helpful. Rather than AI being limited to what it already knows about, it can now understand your specific documents, data, and work context.

For example, by using MCP servers, applications can access your personal documents from Google Drive or data about your codebase from GitHub, providing more personalized and contextually relevant assistance.

Imagine asking an AI assistant: "Summarize last week's team meeting notes and schedule follow-ups with everyone."

By using connections to data sources powered by MCP, the AI assistant can:

* Connect to your Google Drive through an MCP server to read meeting notes
* Understand who needs follow-ups based on the notes
* Connect to your calendar through another MCP server to schedule the meetings automatically

### For developers

MCP reduces development time and complexity when building AI applications that need to access various data sources. With MCP, developers can focus on building great AI experiences rather than repeatedly creating custom connectors.

Traditionally, connecting applications with data sources required building custom, one-off connections for each data source and each application. This created significant duplicative work—every developer wanting to connect their AI application to Google Drive or Slack needed to build their own connection.

MCP simplifies this by enabling developers to build MCP servers for data sources that are then reusable by various applications. For example, using the open source Google Drive MCP server, many different applications can access data from Google Drive without each developer needing to build a custom connection.

This open source ecosystem of MCP servers means developers can leverage existing work rather than starting from scratch, making it easier to build powerful AI applications that seamlessly integrate with the tools and data sources their users already rely on.

## How does MCP work?

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/mcp-simple-diagram.png" />
</Frame>

MCP creates a bridge between your AI applications and your data through a straightforward system:

* **MCP servers** connect to your data sources and tools (like Google Drive or Slack)
* **MCP clients** are run by AI applications (like Claude Desktop) to connect them to these servers
* When you give permission, your AI application discovers available MCP servers
* The AI model can then use these connections to read information and take actions

This modular system means new capabilities can be added without changing AI applications themselves—just like adding new accessories to your computer without upgrading your entire system.

## Who creates and maintains MCP servers?

MCP servers are developed and maintained by:

* Developers at Anthropic who build servers for common tools and data sources
* Open source contributors who create servers for tools they use
* Enterprise development teams building servers for their internal systems
* Software providers making their applications AI-ready

Once an open source MCP server is created for a data source, it can be used by any MCP-compatible AI application, creating a growing ecosystem of connections. See our [list of example servers](https://modelcontextprotocol.io/examples), or [get started building your own server](https://modelcontextprotocol.io/quickstart/server).


# Introduction
Source: https://modelcontextprotocol.io/introduction

Get started with the Model Context Protocol (MCP)

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

## Why MCP?

MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:

* A growing list of pre-built integrations that your LLM can directly plug into
* The flexibility to switch between LLM providers and vendors
* Best practices for securing your data within your infrastructure

### General architecture

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:

```mermaid
flowchart LR
    subgraph "Your Computer"
        Host["Host with MCP Client\n(Claude, IDEs, Tools)"]
        S1["MCP Server A"]
        S2["MCP Server B"]
        S3["MCP Server C"]
        Host <-->|"MCP Protocol"| S1
        Host <-->|"MCP Protocol"| S2
        Host <-->|"MCP Protocol"| S3
        S1 <--> D1[("Local\nData Source A")]
        S2 <--> D2[("Local\nData Source B")]
    end
    subgraph "Internet"
        S3 <-->|"Web APIs"| D3[("Remote\nService C")]
    end
```

* **MCP Hosts**: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
* **MCP Clients**: Protocol clients that maintain 1:1 connections with servers
* **MCP Servers**: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
* **Local Data Sources**: Your computer's files, databases, and services that MCP servers can securely access
* **Remote Services**: External systems available over the internet (e.g., through APIs) that MCP servers can connect to

## Get started

Choose the path that best fits your needs:

### Quick Starts

<CardGroup cols={2}>
  <Card title="For Server Developers" icon="bolt" href="/quickstart/server">
    Get started building your own server to use in Claude for Desktop and other
    clients
  </Card>

  <Card title="For Client Developers" icon="bolt" href="/quickstart/client">
    Get started building your own client that can integrate with all MCP servers
  </Card>

  <Card title="For Claude Desktop Users" icon="bolt" href="/quickstart/user">
    Get started using pre-built servers in Claude for Desktop
  </Card>
</CardGroup>

### Examples

<CardGroup cols={2}>
  <Card title="Example Servers" icon="grid" href="/examples">
    Check out our gallery of official MCP servers and implementations
  </Card>

  <Card title="Example Clients" icon="cubes" href="/clients">
    View the list of clients that support MCP integrations
  </Card>
</CardGroup>

## Tutorials

<CardGroup cols={2}>
  <Card title="Building MCP with LLMs" icon="comments" href="/tutorials/building-mcp-with-llms">
    Learn how to use LLMs like Claude to speed up your MCP development
  </Card>

  <Card title="Debugging Guide" icon="bug" href="/docs/tools/debugging">
    Learn how to effectively debug MCP servers and integrations
  </Card>

  <Card title="MCP Inspector" icon="magnifying-glass" href="/docs/tools/inspector">
    Test and inspect your MCP servers with our interactive debugging tool
  </Card>

  <Card title="MCP Workshop (Video, 2hr)" icon="person-chalkboard" href="https://www.youtube.com/watch?v=kQmXtrmQ5Zg">
    <iframe src="https://www.youtube.com/embed/kQmXtrmQ5Zg" />
  </Card>
</CardGroup>

## Explore MCP

Dive deeper into MCP's core concepts and capabilities:

<CardGroup cols={2}>
  <Card title="Core architecture" icon="sitemap" href="/docs/concepts/architecture">
    Understand how MCP connects clients, servers, and LLMs
  </Card>

  <Card title="Resources" icon="database" href="/docs/concepts/resources">
    Expose data and content from your servers to LLMs
  </Card>

  <Card title="Prompts" icon="message" href="/docs/concepts/prompts">
    Create reusable prompt templates and workflows
  </Card>

  <Card title="Tools" icon="wrench" href="/docs/concepts/tools">
    Enable LLMs to perform actions through your server
  </Card>

  <Card title="Sampling" icon="robot" href="/docs/concepts/sampling">
    Let your servers request completions from LLMs
  </Card>

  <Card title="Transports" icon="network-wired" href="/docs/concepts/transports">
    Learn about MCP's communication mechanism
  </Card>
</CardGroup>

## Contributing

Want to contribute? Check out our [Contributing Guide](/development/contributing) to learn how you can help improve MCP.

## Support and Feedback

Here's how to get help or provide feedback:

* For bug reports and feature requests related to the MCP specification, SDKs, or documentation (open source), please [create a GitHub issue](https://github.com/modelcontextprotocol)
* For discussions or Q\&A about the MCP specification, use the [specification discussions](https://github.com/modelcontextprotocol/specification/discussions)
* For discussions or Q\&A about other MCP open source components, use the [organization discussions](https://github.com/orgs/modelcontextprotocol/discussions)
* For bug reports, feature requests, and questions related to Claude.app and claude.ai's MCP integration, please see Anthropic's guide on [How to Get Support](https://support.anthropic.com/en/articles/9015913-how-to-get-support)


# C# SDK
Source: https://modelcontextprotocol.io/links/sdks/csharp



<Card title="C# SDK" href="https://github.com/modelcontextprotocol/csharp-sdk" />


# Java SDK
Source: https://modelcontextprotocol.io/links/sdks/java



<Card title="Java SDK" href="https://github.com/modelcontextprotocol/java-sdk" />


# Kotlin SDK
Source: https://modelcontextprotocol.io/links/sdks/kotlin



<Card title="Kotlin SDK" href="https://github.com/modelcontextprotocol/kotlin-sdk" />


# Python SDK
Source: https://modelcontextprotocol.io/links/sdks/python



<Card title="Python SDK" href="https://github.com/modelcontextprotocol/python-sdk" />


# Ruby SDK
Source: https://modelcontextprotocol.io/links/sdks/ruby



<Card title="Ruby SDK" href="https://github.com/modelcontextprotocol/ruby-sdk" />


# Swift SDK
Source: https://modelcontextprotocol.io/links/sdks/swift



<Card title="Swift SDK" href="https://github.com/modelcontextprotocol/swift-sdk" />


# TypeScript SDK
Source: https://modelcontextprotocol.io/links/sdks/typescript



<Card title="TypeScript SDK" href="https://github.com/modelcontextprotocol/typescript-sdk" />


# For Client Developers
Source: https://modelcontextprotocol.io/quickstart/client

Get started building your own client that can integrate with all MCP servers.

In this tutorial, you'll learn how to build an LLM-powered chatbot client that connects to MCP servers. It helps to have gone through the [Server quickstart](/quickstart/server) that guides you through the basics of building your first server.

<Tabs>
  <Tab title="Python">
    [You can find the complete code for this tutorial here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/mcp-client-python)

    ## System Requirements

    Before starting, ensure your system meets these requirements:

    * Mac or Windows computer
    * Latest Python version installed
    * Latest version of `uv` installed

    ## Setting Up Your Environment

    First, create a new Python project with `uv`:

    ```bash
    # Create project directory
    uv init mcp-client
    cd mcp-client

    # Create virtual environment
    uv venv

    # Activate virtual environment
    # On Windows:
    .venv\Scripts\activate
    # On Unix or MacOS:
    source .venv/bin/activate

    # Install required packages
    uv add mcp anthropic python-dotenv

    # Remove boilerplate files
    # On Windows:
    del main.py
    # On Unix or MacOS:
    rm main.py

    # Create our main file
    touch client.py
    ```

    ## Setting Up Your API Key

    You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

    Create a `.env` file to store it:

    ```bash
    # Create .env file
    touch .env
    ```

    Add your key to the `.env` file:

    ```bash
    ANTHROPIC_API_KEY=<your key here>
    ```

    Add `.env` to your `.gitignore`:

    ```bash
    echo ".env" >> .gitignore
    ```

    <Warning>
      Make sure you keep your `ANTHROPIC_API_KEY` secure!
    </Warning>

    ## Creating the Client

    ### Basic Client Structure

    First, let's set up our imports and create the basic client class:

    ```python
    import asyncio
    from typing import Optional
    from contextlib import AsyncExitStack

    from mcp import ClientSession, StdioServerParameters
    from mcp.client.stdio import stdio_client

    from anthropic import Anthropic
    from dotenv import load_dotenv

    load_dotenv()  # load environment variables from .env

    class MCPClient:
        def __init__(self):
            # Initialize session and client objects
            self.session: Optional[ClientSession] = None
            self.exit_stack = AsyncExitStack()
            self.anthropic = Anthropic()
        # methods will go here
    ```

    ### Server Connection Management

    Next, we'll implement the method to connect to an MCP server:

    ```python
    async def connect_to_server(self, server_script_path: str):
        """Connect to an MCP server

        Args:
            server_script_path: Path to the server script (.py or .js)
        """
        is_python = server_script_path.endswith('.py')
        is_js = server_script_path.endswith('.js')
        if not (is_python or is_js):
            raise ValueError("Server script must be a .py or .js file")

        command = "python" if is_python else "node"
        server_params = StdioServerParameters(
            command=command,
            args=[server_script_path],
            env=None
        )

        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
        self.stdio, self.write = stdio_transport
        self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))

        await self.session.initialize()

        # List available tools
        response = await self.session.list_tools()
        tools = response.tools
        print("\nConnected to server with tools:", [tool.name for tool in tools])
    ```

    ### Query Processing Logic

    Now let's add the core functionality for processing queries and handling tool calls:

    ```python
    async def process_query(self, query: str) -> str:
        """Process a query using Claude and available tools"""
        messages = [
            {
                "role": "user",
                "content": query
            }
        ]

        response = await self.session.list_tools()
        available_tools = [{
            "name": tool.name,
            "description": tool.description,
            "input_schema": tool.inputSchema
        } for tool in response.tools]

        # Initial Claude API call
        response = self.anthropic.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1000,
            messages=messages,
            tools=available_tools
        )

        # Process response and handle tool calls
        final_text = []

        assistant_message_content = []
        for content in response.content:
            if content.type == 'text':
                final_text.append(content.text)
                assistant_message_content.append(content)
            elif content.type == 'tool_use':
                tool_name = content.name
                tool_args = content.input

                # Execute tool call
                result = await self.session.call_tool(tool_name, tool_args)
                final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")

                assistant_message_content.append(content)
                messages.append({
                    "role": "assistant",
                    "content": assistant_message_content
                })
                messages.append({
                    "role": "user",
                    "content": [
                        {
                            "type": "tool_result",
                            "tool_use_id": content.id,
                            "content": result.content
                        }
                    ]
                })

                # Get next response from Claude
                response = self.anthropic.messages.create(
                    model="claude-3-5-sonnet-20241022",
                    max_tokens=1000,
                    messages=messages,
                    tools=available_tools
                )

                final_text.append(response.content[0].text)

        return "\n".join(final_text)
    ```

    ### Interactive Chat Interface

    Now we'll add the chat loop and cleanup functionality:

    ```python
    async def chat_loop(self):
        """Run an interactive chat loop"""
        print("\nMCP Client Started!")
        print("Type your queries or 'quit' to exit.")

        while True:
            try:
                query = input("\nQuery: ").strip()

                if query.lower() == 'quit':
                    break

                response = await self.process_query(query)
                print("\n" + response)

            except Exception as e:
                print(f"\nError: {str(e)}")

    async def cleanup(self):
        """Clean up resources"""
        await self.exit_stack.aclose()
    ```

    ### Main Entry Point

    Finally, we'll add the main execution logic:

    ```python
    async def main():
        if len(sys.argv) < 2:
            print("Usage: python client.py <path_to_server_script>")
            sys.exit(1)

        client = MCPClient()
        try:
            await client.connect_to_server(sys.argv[1])
            await client.chat_loop()
        finally:
            await client.cleanup()

    if __name__ == "__main__":
        import sys
        asyncio.run(main())
    ```

    You can find the complete `client.py` file [here.](https://gist.github.com/zckly/f3f28ea731e096e53b39b47bf0a2d4b1)

    ## Key Components Explained

    ### 1. Client Initialization

    * The `MCPClient` class initializes with session management and API clients
    * Uses `AsyncExitStack` for proper resource management
    * Configures the Anthropic client for Claude interactions

    ### 2. Server Connection

    * Supports both Python and Node.js servers
    * Validates server script type
    * Sets up proper communication channels
    * Initializes the session and lists available tools

    ### 3. Query Processing

    * Maintains conversation context
    * Handles Claude's responses and tool calls
    * Manages the message flow between Claude and tools
    * Combines results into a coherent response

    ### 4. Interactive Interface

    * Provides a simple command-line interface
    * Handles user input and displays responses
    * Includes basic error handling
    * Allows graceful exit

    ### 5. Resource Management

    * Proper cleanup of resources
    * Error handling for connection issues
    * Graceful shutdown procedures

    ## Common Customization Points

    1. **Tool Handling**

       * Modify `process_query()` to handle specific tool types
       * Add custom error handling for tool calls
       * Implement tool-specific response formatting

    2. **Response Processing**

       * Customize how tool results are formatted
       * Add response filtering or transformation
       * Implement custom logging

    3. **User Interface**
       * Add a GUI or web interface
       * Implement rich console output
       * Add command history or auto-completion

    ## Running the Client

    To run your client with any MCP server:

    ```bash
    uv run client.py path/to/server.py # python server
    uv run client.py path/to/build/index.js # node server
    ```

    <Note>
      If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `python client.py .../quickstart-resources/weather-server-python/weather.py`
    </Note>

    The client will:

    1. Connect to the specified server
    2. List available tools
    3. Start an interactive chat session where you can:
       * Enter queries
       * See tool executions
       * Get responses from Claude

    Here's an example of what it should look like if connected to the weather server from the server quickstart:

    <Frame>
      <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/client-claude-cli-python.png" />
    </Frame>

    ## How It Works

    When you submit a query:

    1. The client gets the list of available tools from the server
    2. Your query is sent to Claude along with tool descriptions
    3. Claude decides which tools (if any) to use
    4. The client executes any requested tool calls through the server
    5. Results are sent back to Claude
    6. Claude provides a natural language response
    7. The response is displayed to you

    ## Best practices

    1. **Error Handling**

       * Always wrap tool calls in try-catch blocks
       * Provide meaningful error messages
       * Gracefully handle connection issues

    2. **Resource Management**

       * Use `AsyncExitStack` for proper cleanup
       * Close connections when done
       * Handle server disconnections

    3. **Security**
       * Store API keys securely in `.env`
       * Validate server responses
       * Be cautious with tool permissions

    ## Troubleshooting

    ### Server Path Issues

    * Double-check the path to your server script is correct
    * Use the absolute path if the relative path isn't working
    * For Windows users, make sure to use forward slashes (/) or escaped backslashes (\\) in the path
    * Verify the server file has the correct extension (.py for Python or .js for Node.js)

    Example of correct path usage:

    ```bash
    # Relative path
    uv run client.py ./server/weather.py

    # Absolute path
    uv run client.py /Users/username/projects/mcp-server/weather.py

    # Windows path (either format works)
    uv run client.py C:/projects/mcp-server/weather.py
    uv run client.py C:\\projects\\mcp-server\\weather.py
    ```

    ### Response Timing

    * The first response might take up to 30 seconds to return
    * This is normal and happens while:
      * The server initializes
      * Claude processes the query
      * Tools are being executed
    * Subsequent responses are typically faster
    * Don't interrupt the process during this initial waiting period

    ### Common Error Messages

    If you see:

    * `FileNotFoundError`: Check your server path
    * `Connection refused`: Ensure the server is running and the path is correct
    * `Tool execution failed`: Verify the tool's required environment variables are set
    * `Timeout error`: Consider increasing the timeout in your client configuration
  </Tab>

  <Tab title="Node">
    [You can find the complete code for this tutorial here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/mcp-client-typescript)

    ## System Requirements

    Before starting, ensure your system meets these requirements:

    * Mac or Windows computer
    * Node.js 17 or higher installed
    * Latest version of `npm` installed
    * Anthropic API key (Claude)

    ## Setting Up Your Environment

    First, let's create and set up our project:

    <CodeGroup>
      ```bash MacOS/Linux
      # Create project directory
      mkdir mcp-client-typescript
      cd mcp-client-typescript

      # Initialize npm project
      npm init -y

      # Install dependencies
      npm install @anthropic-ai/sdk @modelcontextprotocol/sdk dotenv

      # Install dev dependencies
      npm install -D @types/node typescript

      # Create source file
      touch index.ts
      ```

      ```powershell Windows
      # Create project directory
      md mcp-client-typescript
      cd mcp-client-typescript

      # Initialize npm project
      npm init -y

      # Install dependencies
      npm install @anthropic-ai/sdk @modelcontextprotocol/sdk dotenv

      # Install dev dependencies
      npm install -D @types/node typescript

      # Create source file
      new-item index.ts
      ```
    </CodeGroup>

    Update your `package.json` to set `type: "module"` and a build script:

    ```json package.json
    {
      "type": "module",
      "scripts": {
        "build": "tsc && chmod 755 build/index.js"
      }
    }
    ```

    Create a `tsconfig.json` in the root of your project:

    ```json tsconfig.json
    {
      "compilerOptions": {
        "target": "ES2022",
        "module": "Node16",
        "moduleResolution": "Node16",
        "outDir": "./build",
        "rootDir": "./",
        "strict": true,
        "esModuleInterop": true,
        "skipLibCheck": true,
        "forceConsistentCasingInFileNames": true
      },
      "include": ["index.ts"],
      "exclude": ["node_modules"]
    }
    ```

    ## Setting Up Your API Key

    You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

    Create a `.env` file to store it:

    ```bash
    echo "ANTHROPIC_API_KEY=<your key here>" > .env
    ```

    Add `.env` to your `.gitignore`:

    ```bash
    echo ".env" >> .gitignore
    ```

    <Warning>
      Make sure you keep your `ANTHROPIC_API_KEY` secure!
    </Warning>

    ## Creating the Client

    ### Basic Client Structure

    First, let's set up our imports and create the basic client class in `index.ts`:

    ```typescript
    import { Anthropic } from "@anthropic-ai/sdk";
    import {
      MessageParam,
      Tool,
    } from "@anthropic-ai/sdk/resources/messages/messages.mjs";
    import { Client } from "@modelcontextprotocol/sdk/client/index.js";
    import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
    import readline from "readline/promises";
    import dotenv from "dotenv";

    dotenv.config();

    const ANTHROPIC_API_KEY = process.env.ANTHROPIC_API_KEY;
    if (!ANTHROPIC_API_KEY) {
      throw new Error("ANTHROPIC_API_KEY is not set");
    }

    class MCPClient {
      private mcp: Client;
      private anthropic: Anthropic;
      private transport: StdioClientTransport | null = null;
      private tools: Tool[] = [];

      constructor() {
        this.anthropic = new Anthropic({
          apiKey: ANTHROPIC_API_KEY,
        });
        this.mcp = new Client({ name: "mcp-client-cli", version: "1.0.0" });
      }
      // methods will go here
    }
    ```

    ### Server Connection Management

    Next, we'll implement the method to connect to an MCP server:

    ```typescript
    async connectToServer(serverScriptPath: string) {
      try {
        const isJs = serverScriptPath.endsWith(".js");
        const isPy = serverScriptPath.endsWith(".py");
        if (!isJs && !isPy) {
          throw new Error("Server script must be a .js or .py file");
        }
        const command = isPy
          ? process.platform === "win32"
            ? "python"
            : "python3"
          : process.execPath;

        this.transport = new StdioClientTransport({
          command,
          args: [serverScriptPath],
        });
        await this.mcp.connect(this.transport);

        const toolsResult = await this.mcp.listTools();
        this.tools = toolsResult.tools.map((tool) => {
          return {
            name: tool.name,
            description: tool.description,
            input_schema: tool.inputSchema,
          };
        });
        console.log(
          "Connected to server with tools:",
          this.tools.map(({ name }) => name)
        );
      } catch (e) {
        console.log("Failed to connect to MCP server: ", e);
        throw e;
      }
    }
    ```

    ### Query Processing Logic

    Now let's add the core functionality for processing queries and handling tool calls:

    ```typescript
    async processQuery(query: string) {
      const messages: MessageParam[] = [
        {
          role: "user",
          content: query,
        },
      ];

      const response = await this.anthropic.messages.create({
        model: "claude-3-5-sonnet-20241022",
        max_tokens: 1000,
        messages,
        tools: this.tools,
      });

      const finalText = [];

      for (const content of response.content) {
        if (content.type === "text") {
          finalText.push(content.text);
        } else if (content.type === "tool_use") {
          const toolName = content.name;
          const toolArgs = content.input as { [x: string]: unknown } | undefined;

          const result = await this.mcp.callTool({
            name: toolName,
            arguments: toolArgs,
          });
          finalText.push(
            `[Calling tool ${toolName} with args ${JSON.stringify(toolArgs)}]`
          );

          messages.push({
            role: "user",
            content: result.content as string,
          });

          const response = await this.anthropic.messages.create({
            model: "claude-3-5-sonnet-20241022",
            max_tokens: 1000,
            messages,
          });

          finalText.push(
            response.content[0].type === "text" ? response.content[0].text : ""
          );
        }
      }

      return finalText.join("\n");
    }
    ```

    ### Interactive Chat Interface

    Now we'll add the chat loop and cleanup functionality:

    ```typescript
    async chatLoop() {
      const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
      });

      try {
        console.log("\nMCP Client Started!");
        console.log("Type your queries or 'quit' to exit.");

        while (true) {
          const message = await rl.question("\nQuery: ");
          if (message.toLowerCase() === "quit") {
            break;
          }
          const response = await this.processQuery(message);
          console.log("\n" + response);
        }
      } finally {
        rl.close();
      }
    }

    async cleanup() {
      await this.mcp.close();
    }
    ```

    ### Main Entry Point

    Finally, we'll add the main execution logic:

    ```typescript
    async function main() {
      if (process.argv.length < 3) {
        console.log("Usage: node index.ts <path_to_server_script>");
        return;
      }
      const mcpClient = new MCPClient();
      try {
        await mcpClient.connectToServer(process.argv[2]);
        await mcpClient.chatLoop();
      } finally {
        await mcpClient.cleanup();
        process.exit(0);
      }
    }

    main();
    ```

    ## Running the Client

    To run your client with any MCP server:

    ```bash
    # Build TypeScript
    npm run build

    # Run the client
    node build/index.js path/to/server.py # python server
    node build/index.js path/to/build/index.js # node server
    ```

    <Note>
      If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `node build/index.js .../quickstart-resources/weather-server-typescript/build/index.js`
    </Note>

    **The client will:**

    1. Connect to the specified server
    2. List available tools
    3. Start an interactive chat session where you can:
       * Enter queries
       * See tool executions
       * Get responses from Claude

    ## How It Works

    When you submit a query:

    1. The client gets the list of available tools from the server
    2. Your query is sent to Claude along with tool descriptions
    3. Claude decides which tools (if any) to use
    4. The client executes any requested tool calls through the server
    5. Results are sent back to Claude
    6. Claude provides a natural language response
    7. The response is displayed to you

    ## Best practices

    1. **Error Handling**

       * Use TypeScript's type system for better error detection
       * Wrap tool calls in try-catch blocks
       * Provide meaningful error messages
       * Gracefully handle connection issues

    2. **Security**
       * Store API keys securely in `.env`
       * Validate server responses
       * Be cautious with tool permissions

    ## Troubleshooting

    ### Server Path Issues

    * Double-check the path to your server script is correct
    * Use the absolute path if the relative path isn't working
    * For Windows users, make sure to use forward slashes (/) or escaped backslashes (\\) in the path
    * Verify the server file has the correct extension (.js for Node.js or .py for Python)

    Example of correct path usage:

    ```bash
    # Relative path
    node build/index.js ./server/build/index.js

    # Absolute path
    node build/index.js /Users/username/projects/mcp-server/build/index.js

    # Windows path (either format works)
    node build/index.js C:/projects/mcp-server/build/index.js
    node build/index.js C:\\projects\\mcp-server\\build\\index.js
    ```

    ### Response Timing

    * The first response might take up to 30 seconds to return
    * This is normal and happens while:
      * The server initializes
      * Claude processes the query
      * Tools are being executed
    * Subsequent responses are typically faster
    * Don't interrupt the process during this initial waiting period

    ### Common Error Messages

    If you see:

    * `Error: Cannot find module`: Check your build folder and ensure TypeScript compilation succeeded
    * `Connection refused`: Ensure the server is running and the path is correct
    * `Tool execution failed`: Verify the tool's required environment variables are set
    * `ANTHROPIC_API_KEY is not set`: Check your .env file and environment variables
    * `TypeError`: Ensure you're using the correct types for tool arguments
  </Tab>

  <Tab title="Java">
    <Note>
      This is a quickstart demo based on Spring AI MCP auto-configuration and boot starters.
      To learn how to create sync and async MCP Clients manually, consult the [Java SDK Client](/sdk/java/mcp-client) documentation
    </Note>

    This example demonstrates how to build an interactive chatbot that combines Spring AI's Model Context Protocol (MCP) with the [Brave Search MCP Server](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/brave-search). The application creates a conversational interface powered by Anthropic's Claude AI model that can perform internet searches through Brave Search, enabling natural language interactions with real-time web data.
    [You can find the complete code for this tutorial here.](https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/web-search/brave-chatbot)

    ## System Requirements

    Before starting, ensure your system meets these requirements:

    * Java 17 or higher
    * Maven 3.6+
    * npx package manager
    * Anthropic API key (Claude)
    * Brave Search API key

    ## Setting Up Your Environment

    1. Install npx (Node Package eXecute):
       First, make sure to install [npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm)
       and then run:

       ```bash
       npm install -g npx
       ```

    2. Clone the repository:

       ```bash
       git clone https://github.com/spring-projects/spring-ai-examples.git
       cd model-context-protocol/brave-chatbot
       ```

    3. Set up your API keys:

       ```bash
       export ANTHROPIC_API_KEY='your-anthropic-api-key-here'
       export BRAVE_API_KEY='your-brave-api-key-here'
       ```

    4. Build the application:

       ```bash
       ./mvnw clean install
       ```

    5. Run the application using Maven:
       ```bash
       ./mvnw spring-boot:run
       ```

    <Warning>
      Make sure you keep your `ANTHROPIC_API_KEY` and `BRAVE_API_KEY` keys secure!
    </Warning>

    ## How it Works

    The application integrates Spring AI with the Brave Search MCP server through several components:

    ### MCP Client Configuration

    1. Required dependencies in pom.xml:

    ```xml
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-mcp-client</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-anthropic</artifactId>
    </dependency>
    ```

    2. Application properties (application.yml):

    ```yml
    spring:
      ai:
        mcp:
          client:
            enabled: true
            name: brave-search-client
            version: 1.0.0
            type: SYNC
            request-timeout: 20s
            stdio:
              root-change-notification: true
              servers-configuration: classpath:/mcp-servers-config.json
            toolcallback:
              enabled: true
        anthropic:
          api-key: ${ANTHROPIC_API_KEY}
    ```

    This activates the `spring-ai-starter-mcp-client` to create one or more `McpClient`s based on the provided server configuration.
    The `spring.ai.mcp.client.toolcallback.enabled=true` property enables the tool callback mechanism, that automatically registers all MCP tool as spring ai tools.
    It is disabled by default.

    3. MCP Server Configuration (`mcp-servers-config.json`):

    ```json
    {
      "mcpServers": {
        "brave-search": {
          "command": "npx",
          "args": ["-y", "@modelcontextprotocol/server-brave-search"],
          "env": {
            "BRAVE_API_KEY": "<PUT YOUR BRAVE API KEY>"
          }
        }
      }
    }
    ```

    ### Chat Implementation

    The chatbot is implemented using Spring AI's ChatClient with MCP tool integration:

    ```java
    var chatClient = chatClientBuilder
        .defaultSystem("You are useful assistant, expert in AI and Java.")
        .defaultToolCallbacks((Object[]) mcpToolAdapter.toolCallbacks())
        .defaultAdvisors(new MessageChatMemoryAdvisor(new InMemoryChatMemory()))
        .build();
    ```

    <Warning>
      Breaking change: From SpringAI 1.0.0-M8 onwards, use `.defaultToolCallbacks(...)` instead of `.defaultTool(...)` to register MCP tools.
    </Warning>

    Key features:

    * Uses Claude AI model for natural language understanding
    * Integrates Brave Search through MCP for real-time web search capabilities
    * Maintains conversation memory using InMemoryChatMemory
    * Runs as an interactive command-line application

    ### Build and run

    ```bash
    ./mvnw clean install
    java -jar ./target/ai-mcp-brave-chatbot-0.0.1-SNAPSHOT.jar
    ```

    or

    ```bash
    ./mvnw spring-boot:run
    ```

    The application will start an interactive chat session where you can ask questions. The chatbot will use Brave Search when it needs to find information from the internet to answer your queries.

    The chatbot can:

    * Answer questions using its built-in knowledge
    * Perform web searches when needed using Brave Search
    * Remember context from previous messages in the conversation
    * Combine information from multiple sources to provide comprehensive answers

    ### Advanced Configuration

    The MCP client supports additional configuration options:

    * Client customization through `McpSyncClientCustomizer` or `McpAsyncClientCustomizer`
    * Multiple clients with multiple transport types: `STDIO` and `SSE` (Server-Sent Events)
    * Integration with Spring AI's tool execution framework
    * Automatic client initialization and lifecycle management

    For WebFlux-based applications, you can use the WebFlux starter instead:

    ```xml
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-mcp-client-webflux-spring-boot-starter</artifactId>
    </dependency>
    ```

    This provides similar functionality but uses a WebFlux-based SSE transport implementation, recommended for production deployments.
  </Tab>

  <Tab title="Kotlin">
    [You can find the complete code for this tutorial here.](https://github.com/modelcontextprotocol/kotlin-sdk/tree/main/samples/kotlin-mcp-client)

    ## System Requirements

    Before starting, ensure your system meets these requirements:

    * Java 17 or higher
    * Anthropic API key (Claude)

    ## Setting up your environment

    First, let's install `java` and `gradle` if you haven't already.
    You can download `java` from [official Oracle JDK website](https://www.oracle.com/java/technologies/downloads/).
    Verify your `java` installation:

    ```bash
    java --version
    ```

    Now, let's create and set up your project:

    <CodeGroup>
      ```bash MacOS/Linux
      # Create a new directory for our project
      mkdir kotlin-mcp-client
      cd kotlin-mcp-client

      # Initialize a new kotlin project
      gradle init
      ```

      ```powershell Windows
      # Create a new directory for our project
      md kotlin-mcp-client
      cd kotlin-mcp-client
      # Initialize a new kotlin project
      gradle init
      ```
    </CodeGroup>

    After running `gradle init`, you will be presented with options for creating your project.
    Select **Application** as the project type, **Kotlin** as the programming language, and **Java 17** as the Java version.

    Alternatively, you can create a Kotlin application using the [IntelliJ IDEA project wizard](https://kotlinlang.org/docs/jvm-get-started.html).

    After creating the project, add the following dependencies:

    <CodeGroup>
      ```kotlin build.gradle.kts
      val mcpVersion = "0.4.0"
      val slf4jVersion = "2.0.9"
      val anthropicVersion = "0.8.0"

      dependencies {
          implementation("io.modelcontextprotocol:kotlin-sdk:$mcpVersion")
          implementation("org.slf4j:slf4j-nop:$slf4jVersion")
          implementation("com.anthropic:anthropic-java:$anthropicVersion")
      }
      ```

      ```groovy build.gradle
      def mcpVersion = '0.3.0'
      def slf4jVersion = '2.0.9'
      def anthropicVersion = '0.8.0'
      dependencies {
          implementation "io.modelcontextprotocol:kotlin-sdk:$mcpVersion"
          implementation "org.slf4j:slf4j-nop:$slf4jVersion"
          implementation "com.anthropic:anthropic-java:$anthropicVersion"
      }
      ```
    </CodeGroup>

    Also, add the following plugins to your build script:

    <CodeGroup>
      ```kotlin build.gradle.kts
      plugins {
          id("com.github.johnrengelman.shadow") version "8.1.1"
      }
      ```

      ```groovy build.gradle
      plugins {
          id 'com.github.johnrengelman.shadow' version '8.1.1'
      }
      ```
    </CodeGroup>

    ## Setting up your API key

    You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

    Set up your API key:

    ```bash
    export ANTHROPIC_API_KEY='your-anthropic-api-key-here'
    ```

    <Warning>
      Make sure your keep your `ANTHROPIC_API_KEY` secure!
    </Warning>

    ## Creating the Client

    ### Basic Client Structure

    First, let's create the basic client class:

    ```kotlin
    class MCPClient : AutoCloseable {
        private val anthropic = AnthropicOkHttpClient.fromEnv()
        private val mcp: Client = Client(clientInfo = Implementation(name = "mcp-client-cli", version = "1.0.0"))
        private lateinit var tools: List<ToolUnion>

        // methods will go here

        override fun close() {
            runBlocking {
                mcp.close()
                anthropic.close()
            }
        }
    ```

    ### Server connection management

    Next, we'll implement the method to connect to an MCP server:

    ```kotlin
    suspend fun connectToServer(serverScriptPath: String) {
        try {
            val command = buildList {
                when (serverScriptPath.substringAfterLast(".")) {
                    "js" -> add("node")
                    "py" -> add(if (System.getProperty("os.name").lowercase().contains("win")) "python" else "python3")
                    "jar" -> addAll(listOf("java", "-jar"))
                    else -> throw IllegalArgumentException("Server script must be a .js, .py or .jar file")
                }
                add(serverScriptPath)
            }

            val process = ProcessBuilder(command).start()
            val transport = StdioClientTransport(
                input = process.inputStream.asSource().buffered(),
                output = process.outputStream.asSink().buffered()
            )

            mcp.connect(transport)

            val toolsResult = mcp.listTools()
            tools = toolsResult?.tools?.map { tool ->
                ToolUnion.ofTool(
                    Tool.builder()
                        .name(tool.name)
                        .description(tool.description ?: "")
                        .inputSchema(
                            Tool.InputSchema.builder()
                                .type(JsonValue.from(tool.inputSchema.type))
                                .properties(tool.inputSchema.properties.toJsonValue())
                                .putAdditionalProperty("required", JsonValue.from(tool.inputSchema.required))
                                .build()
                        )
                        .build()
                )
            } ?: emptyList()
            println("Connected to server with tools: ${tools.joinToString(", ") { it.tool().get().name() }}")
        } catch (e: Exception) {
            println("Failed to connect to MCP server: $e")
            throw e
        }
    }
    ```

    Also create a helper function to convert from `JsonObject` to `JsonValue` for Anthropic:

    ```kotlin
    private fun JsonObject.toJsonValue(): JsonValue {
        val mapper = ObjectMapper()
        val node = mapper.readTree(this.toString())
        return JsonValue.fromJsonNode(node)
    }
    ```

    ### Query processing logic

    Now let's add the core functionality for processing queries and handling tool calls:

    ```kotlin
    private val messageParamsBuilder: MessageCreateParams.Builder = MessageCreateParams.builder()
        .model(Model.CLAUDE_3_5_SONNET_20241022)
        .maxTokens(1024)

    suspend fun processQuery(query: String): String {
        val messages = mutableListOf(
            MessageParam.builder()
                .role(MessageParam.Role.USER)
                .content(query)
                .build()
        )

        val response = anthropic.messages().create(
            messageParamsBuilder
                .messages(messages)
                .tools(tools)
                .build()
        )

        val finalText = mutableListOf<String>()
        response.content().forEach { content ->
            when {
                content.isText() -> finalText.add(content.text().getOrNull()?.text() ?: "")

                content.isToolUse() -> {
                    val toolName = content.toolUse().get().name()
                    val toolArgs =
                        content.toolUse().get()._input().convert(object : TypeReference<Map<String, JsonValue>>() {})

                    val result = mcp.callTool(
                        name = toolName,
                        arguments = toolArgs ?: emptyMap()
                    )
                    finalText.add("[Calling tool $toolName with args $toolArgs]")

                    messages.add(
                        MessageParam.builder()
                            .role(MessageParam.Role.USER)
                            .content(
                                """
                                    "type": "tool_result",
                                    "tool_name": $toolName,
                                    "result": ${result?.content?.joinToString("\n") { (it as TextContent).text ?: "" }}
                                """.trimIndent()
                            )
                            .build()
                    )

                    val aiResponse = anthropic.messages().create(
                        messageParamsBuilder
                            .messages(messages)
                            .build()
                    )

                    finalText.add(aiResponse.content().first().text().getOrNull()?.text() ?: "")
                }
            }
        }

        return finalText.joinToString("\n", prefix = "", postfix = "")
    }
    ```

    ### Interactive chat

    We'll add the chat loop:

    ```kotlin
    suspend fun chatLoop() {
        println("\nMCP Client Started!")
        println("Type your queries or 'quit' to exit.")

        while (true) {
            print("\nQuery: ")
            val message = readLine() ?: break
            if (message.lowercase() == "quit") break
            val response = processQuery(message)
            println("\n$response")
        }
    }
    ```

    ### Main entry point

    Finally, we'll add the main execution function:

    ```kotlin
    fun main(args: Array<String>) = runBlocking {
        if (args.isEmpty()) throw IllegalArgumentException("Usage: java -jar <your_path>/build/libs/kotlin-mcp-client-0.1.0-all.jar <path_to_server_script>")
        val serverPath = args.first()
        val client = MCPClient()
        client.use {
            client.connectToServer(serverPath)
            client.chatLoop()
        }
    }
    ```

    ## Running the client

    To run your client with any MCP server:

    ```bash
    ./gradlew build

    # Run the client
    java -jar build/libs/<your-jar-name>.jar path/to/server.jar # jvm server
    java -jar build/libs/<your-jar-name>.jar path/to/server.py # python server
    java -jar build/libs/<your-jar-name>.jar path/to/build/index.js # node server
    ```

    <Note>
      If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `java -jar build/libs/kotlin-mcp-client-0.1.0-all.jar .../samples/weather-stdio-server/build/libs/weather-stdio-server-0.1.0-all.jar`
    </Note>

    **The client will:**

    1. Connect to the specified server
    2. List available tools
    3. Start an interactive chat session where you can:
       * Enter queries
       * See tool executions
       * Get responses from Claude

    ## How it works

    Here's a high-level workflow schema:

    ```mermaid
    ---
    config:
        theme: neutral
    ---
    sequenceDiagram
        actor User
        participant Client
        participant Claude
        participant MCP_Server as MCP Server
        participant Tools

        User->>Client: Send query
        Client<<->>MCP_Server: Get available tools
        Client->>Claude: Send query with tool descriptions
        Claude-->>Client: Decide tool execution
        Client->>MCP_Server: Request tool execution
        MCP_Server->>Tools: Execute chosen tools
        Tools-->>MCP_Server: Return results
        MCP_Server-->>Client: Send results
        Client->>Claude: Send tool results
        Claude-->>Client: Provide final response
        Client-->>User: Display response
    ```

    When you submit a query:

    1. The client gets the list of available tools from the server
    2. Your query is sent to Claude along with tool descriptions
    3. Claude decides which tools (if any) to use
    4. The client executes any requested tool calls through the server
    5. Results are sent back to Claude
    6. Claude provides a natural language response
    7. The response is displayed to you

    ## Best practices

    1. **Error Handling**

       * Leverage Kotlin's type system to model errors explicitly
       * Wrap external tool and API calls in `try-catch` blocks when exceptions are possible
       * Provide clear and meaningful error messages
       * Handle network timeouts and connection issues gracefully

    2. **Security**
       * Store API keys and secrets securely in `local.properties`, environment variables, or secret managers
       * Validate all external responses to avoid unexpected or unsafe data usage
       * Be cautious with permissions and trust boundaries when using tools

    ## Troubleshooting

    ### Server Path Issues

    * Double-check the path to your server script is correct
    * Use the absolute path if the relative path isn't working
    * For Windows users, make sure to use forward slashes (/) or escaped backslashes (\\) in the path
    * Make sure that the required runtime is installed (java for Java, npm for Node.js, or uv for Python)
    * Verify the server file has the correct extension (.jar for Java, .js for Node.js or .py for Python)

    Example of correct path usage:

    ```bash
    # Relative path
    java -jar build/libs/client.jar ./server/build/libs/server.jar

    # Absolute path
    java -jar build/libs/client.jar /Users/username/projects/mcp-server/build/libs/server.jar

    # Windows path (either format works)
    java -jar build/libs/client.jar C:/projects/mcp-server/build/libs/server.jar
    java -jar build/libs/client.jar C:\\projects\\mcp-server\\build\\libs\\server.jar
    ```

    ### Response Timing

    * The first response might take up to 30 seconds to return
    * This is normal and happens while:
      * The server initializes
      * Claude processes the query
      * Tools are being executed
    * Subsequent responses are typically faster
    * Don't interrupt the process during this initial waiting period

    ### Common Error Messages

    If you see:

    * `Connection refused`: Ensure the server is running and the path is correct
    * `Tool execution failed`: Verify the tool's required environment variables are set
    * `ANTHROPIC_API_KEY is not set`: Check your environment variables
  </Tab>

  <Tab title="C#">
    [You can find the complete code for this tutorial here.](https://github.com/modelcontextprotocol/csharp-sdk/tree/main/samples/QuickstartClient)

    ## System Requirements

    Before starting, ensure your system meets these requirements:

    * .NET 8.0 or higher
    * Anthropic API key (Claude)
    * Windows, Linux, or MacOS

    ## Setting up your environment

    First, create a new .NET project:

    ```bash
    dotnet new console -n QuickstartClient
    cd QuickstartClient
    ```

    Then, add the required dependencies to your project:

    ```bash
    dotnet add package ModelContextProtocol --prerelease
    dotnet add package Anthropic.SDK
    dotnet add package Microsoft.Extensions.Hosting
    dotnet add package Microsoft.Extensions.AI
    ```

    ## Setting up your API key

    You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

    ```bash
    dotnet user-secrets init
    dotnet user-secrets set "ANTHROPIC_API_KEY" "<your key here>"
    ```

    ## Creating the Client

    ### Basic Client Structure

    First, let's setup the basic client class in the file `Program.cs`:

    ```csharp
    using Anthropic.SDK;
    using Microsoft.Extensions.AI;
    using Microsoft.Extensions.Configuration;
    using Microsoft.Extensions.Hosting;
    using ModelContextProtocol.Client;
    using ModelContextProtocol.Protocol.Transport;

    var builder = Host.CreateApplicationBuilder(args);

    builder.Configuration
        .AddEnvironmentVariables()
        .AddUserSecrets<Program>();
    ```

    This creates the beginnings of a .NET console application that can read the API key from user secrets.

    Next, we'll setup the MCP Client:

    ```csharp
    var (command, arguments) = GetCommandAndArguments(args);

    var clientTransport = new StdioClientTransport(new()
    {
        Name = "Demo Server",
        Command = command,
        Arguments = arguments,
    });

    await using var mcpClient = await McpClientFactory.CreateAsync(clientTransport);

    var tools = await mcpClient.ListToolsAsync();
    foreach (var tool in tools)
    {
        Console.WriteLine($"Connected to server with tools: {tool.Name}");
    }
    ```

    Add this function at the end of the `Program.cs` file:

    ```csharp
    static (string command, string[] arguments) GetCommandAndArguments(string[] args)
    {
        return args switch
        {
            [var script] when script.EndsWith(".py") => ("python", args),
            [var script] when script.EndsWith(".js") => ("node", args),
            [var script] when Directory.Exists(script) || (File.Exists(script) && script.EndsWith(".csproj")) => ("dotnet", ["run", "--project", script, "--no-build"]),
            _ => throw new NotSupportedException("An unsupported server script was provided. Supported scripts are .py, .js, or .csproj")
        };
    }
    ```

    This creates a MCP client that will connect to a server that is provided as a command line argument. It then lists the available tools from the connected server.

    ### Query processing logic

    Now let's add the core functionality for processing queries and handling tool calls:

    ```csharp
    using var anthropicClient = new AnthropicClient(new APIAuthentication(builder.Configuration["ANTHROPIC_API_KEY"]))
        .Messages
        .AsBuilder()
        .UseFunctionInvocation()
        .Build();

    var options = new ChatOptions
    {
        MaxOutputTokens = 1000,
        ModelId = "claude-3-5-sonnet-20241022",
        Tools = [.. tools]
    };

    Console.ForegroundColor = ConsoleColor.Green;
    Console.WriteLine("MCP Client Started!");
    Console.ResetColor();

    PromptForInput();
    while(Console.ReadLine() is string query && !"exit".Equals(query, StringComparison.OrdinalIgnoreCase))
    {
        if (string.IsNullOrWhiteSpace(query))
        {
            PromptForInput();
            continue;
        }

        await foreach (var message in anthropicClient.GetStreamingResponseAsync(query, options))
        {
            Console.Write(message);
        }
        Console.WriteLine();

        PromptForInput();
    }

    static void PromptForInput()
    {
        Console.WriteLine("Enter a command (or 'exit' to quit):");
        Console.ForegroundColor = ConsoleColor.Cyan;
        Console.Write("> ");
        Console.ResetColor();
    }
    ```

    ## Key Components Explained

    ### 1. Client Initialization

    * The client is initialized using `McpClientFactory.CreateAsync()`, which sets up the transport type and command to run the server.

    ### 2. Server Connection

    * Supports Python, Node.js, and .NET servers.
    * The server is started using the command specified in the arguments.
    * Configures to use stdio for communication with the server.
    * Initializes the session and available tools.

    ### 3. Query Processing

    * Leverages [Microsoft.Extensions.AI](https://learn.microsoft.com/dotnet/ai/ai-extensions) for the chat client.
    * Configures the `IChatClient` to use automatic tool (function) invocation.
    * The client reads user input and sends it to the server.
    * The server processes the query and returns a response.
    * The response is displayed to the user.

    ## Running the Client

    To run your client with any MCP server:

    ```bash
    dotnet run -- path/to/server.csproj # dotnet server
    dotnet run -- path/to/server.py # python server
    dotnet run -- path/to/server.js # node server
    ```

    <Note>
      If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `dotnet run -- path/to/QuickstartWeatherServer`.
    </Note>

    The client will:

    1. Connect to the specified server
    2. List available tools
    3. Start an interactive chat session where you can:
       * Enter queries
       * See tool executions
       * Get responses from Claude
    4. Exit the session when done

    Here's an example of what it should look like it connected to a weather server quickstart:

    <Frame>
      <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-dotnet-client.png" />
    </Frame>
  </Tab>
</Tabs>

## Next steps

<CardGroup cols={2}>
  <Card title="Example servers" icon="grid" href="/examples">
    Check out our gallery of official MCP servers and implementations
  </Card>

  <Card title="Clients" icon="cubes" href="/clients">
    View the list of clients that support MCP integrations
  </Card>

  <Card title="Building MCP with LLMs" icon="comments" href="/tutorials/building-mcp-with-llms">
    Learn how to use LLMs like Claude to speed up your MCP development
  </Card>

  <Card title="Core architecture" icon="sitemap" href="/docs/concepts/architecture">
    Understand how MCP connects clients, servers, and LLMs
  </Card>
</CardGroup>


# For Server Developers
Source: https://modelcontextprotocol.io/quickstart/server

Get started building your own server to use in Claude for Desktop and other clients.

In this tutorial, we'll build a simple MCP weather server and connect it to a host, Claude for Desktop. We'll start with a basic setup, and then progress to more complex use cases.

### What we'll be building

Many LLMs do not currently have the ability to fetch the forecast and severe weather alerts. Let's use MCP to solve that!

We'll build a server that exposes two tools: `get-alerts` and `get-forecast`. Then we'll connect the server to an MCP host (in this case, Claude for Desktop):

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/weather-alerts.png" />
</Frame>

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/current-weather.png" />
</Frame>

<Note>
  Servers can connect to any client. We've chosen Claude for Desktop here for simplicity, but we also have guides on [building your own client](/quickstart/client) as well as a [list of other clients here](/clients).
</Note>

### Core MCP Concepts

MCP servers can provide three main types of capabilities:

1. **Resources**: File-like data that can be read by clients (like API responses or file contents)
2. **Tools**: Functions that can be called by the LLM (with user approval)
3. **Prompts**: Pre-written templates that help users accomplish specific tasks

This tutorial will primarily focus on tools.

<Tabs>
  <Tab title="Python">
    Let's get started with building our weather server! [You can find the complete code for what we'll be building here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/weather-server-python)

    ### Prerequisite knowledge

    This quickstart assumes you have familiarity with:

    * Python
    * LLMs like Claude

    ### System requirements

    * Python 3.10 or higher installed.
    * You must use the Python MCP SDK 1.2.0 or higher.

    ### Set up your environment

    First, let's install `uv` and set up our Python project and environment:

    <CodeGroup>
      ```bash MacOS/Linux
      curl -LsSf https://astral.sh/uv/install.sh | sh
      ```

      ```powershell Windows
      powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
      ```
    </CodeGroup>

    Make sure to restart your terminal afterwards to ensure that the `uv` command gets picked up.

    Now, let's create and set up our project:

    <CodeGroup>
      ```bash MacOS/Linux
      # Create a new directory for our project
      uv init weather
      cd weather

      # Create virtual environment and activate it
      uv venv
      source .venv/bin/activate

      # Install dependencies
      uv add "mcp[cli]" httpx

      # Create our server file
      touch weather.py
      ```

      ```powershell Windows
      # Create a new directory for our project
      uv init weather
      cd weather

      # Create virtual environment and activate it
      uv venv
      .venv\Scripts\activate

      # Install dependencies
      uv add mcp[cli] httpx

      # Create our server file
      new-item weather.py
      ```
    </CodeGroup>

    Now let's dive into building your server.

    ## Building your server

    ### Importing packages and setting up the instance

    Add these to the top of your `weather.py`:

    ```python
    from typing import Any
    import httpx
    from mcp.server.fastmcp import FastMCP

    # Initialize FastMCP server
    mcp = FastMCP("weather")

    # Constants
    NWS_API_BASE = "https://api.weather.gov"
    USER_AGENT = "weather-app/1.0"
    ```

    The FastMCP class uses Python type hints and docstrings to automatically generate tool definitions, making it easy to create and maintain MCP tools.

    ### Helper functions

    Next, let's add our helper functions for querying and formatting the data from the National Weather Service API:

    ```python
    async def make_nws_request(url: str) -> dict[str, Any] | None:
        """Make a request to the NWS API with proper error handling."""
        headers = {
            "User-Agent": USER_AGENT,
            "Accept": "application/geo+json"
        }
        async with httpx.AsyncClient() as client:
            try:
                response = await client.get(url, headers=headers, timeout=30.0)
                response.raise_for_status()
                return response.json()
            except Exception:
                return None

    def format_alert(feature: dict) -> str:
        """Format an alert feature into a readable string."""
        props = feature["properties"]
        return f"""
    Event: {props.get('event', 'Unknown')}
    Area: {props.get('areaDesc', 'Unknown')}
    Severity: {props.get('severity', 'Unknown')}
    Description: {props.get('description', 'No description available')}
    Instructions: {props.get('instruction', 'No specific instructions provided')}
    """
    ```

    ### Implementing tool execution

    The tool execution handler is responsible for actually executing the logic of each tool. Let's add it:

    ```python
    @mcp.tool()
    async def get_alerts(state: str) -> str:
        """Get weather alerts for a US state.

        Args:
            state: Two-letter US state code (e.g. CA, NY)
        """
        url = f"{NWS_API_BASE}/alerts/active/area/{state}"
        data = await make_nws_request(url)

        if not data or "features" not in data:
            return "Unable to fetch alerts or no alerts found."

        if not data["features"]:
            return "No active alerts for this state."

        alerts = [format_alert(feature) for feature in data["features"]]
        return "\n---\n".join(alerts)

    @mcp.tool()
    async def get_forecast(latitude: float, longitude: float) -> str:
        """Get weather forecast for a location.

        Args:
            latitude: Latitude of the location
            longitude: Longitude of the location
        """
        # First get the forecast grid endpoint
        points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
        points_data = await make_nws_request(points_url)

        if not points_data:
            return "Unable to fetch forecast data for this location."

        # Get the forecast URL from the points response
        forecast_url = points_data["properties"]["forecast"]
        forecast_data = await make_nws_request(forecast_url)

        if not forecast_data:
            return "Unable to fetch detailed forecast."

        # Format the periods into a readable forecast
        periods = forecast_data["properties"]["periods"]
        forecasts = []
        for period in periods[:5]:  # Only show next 5 periods
            forecast = f"""
    {period['name']}:
    Temperature: {period['temperature']}°{period['temperatureUnit']}
    Wind: {period['windSpeed']} {period['windDirection']}
    Forecast: {period['detailedForecast']}
    """
            forecasts.append(forecast)

        return "\n---\n".join(forecasts)
    ```

    ### Running the server

    Finally, let's initialize and run the server:

    ```python
    if __name__ == "__main__":
        # Initialize and run the server
        mcp.run(transport='stdio')
    ```

    Your server is complete! Run `uv run weather.py` to confirm that everything's working.

    Let's now test your server from an existing MCP host, Claude for Desktop.

    ## Testing your server with Claude for Desktop

    <Note>
      Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/quickstart/client) tutorial to build an MCP client that connects to the server we just built.
    </Note>

    First, make sure you have Claude for Desktop installed. [You can install the latest version
    here.](https://claude.ai/download) If you already have Claude for Desktop, **make sure it's updated to the latest version.**

    We'll need to configure Claude for Desktop for whichever MCP servers you want to use. To do this, open your Claude for Desktop App configuration at `~/Library/Application Support/Claude/claude_desktop_config.json` in a text editor. Make sure to create the file if it doesn't exist.

    For example, if you have [VS Code](https://code.visualstudio.com/) installed:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```bash
        code ~/Library/Application\ Support/Claude/claude_desktop_config.json
        ```
      </Tab>

      <Tab title="Windows">
        ```powershell
        code $env:AppData\Claude\claude_desktop_config.json
        ```
      </Tab>
    </Tabs>

    You'll then add your servers in the `mcpServers` key. The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.

    In this case, we'll add our single weather server like so:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```json Python
        {
          "mcpServers": {
            "weather": {
              "command": "uv",
              "args": [
                "--directory",
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather",
                "run",
                "weather.py"
              ]
            }
          }
        }
        ```
      </Tab>

      <Tab title="Windows">
        ```json Python
        {
          "mcpServers": {
            "weather": {
              "command": "uv",
              "args": [
                "--directory",
                "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\weather",
                "run",
                "weather.py"
              ]
            }
          }
        }
        ```
      </Tab>
    </Tabs>

    <Warning>
      You may need to put the full path to the `uv` executable in the `command` field. You can get this by running `which uv` on MacOS/Linux or `where uv` on Windows.
    </Warning>

    <Note>
      Make sure you pass in the absolute path to your server.
    </Note>

    This tells Claude for Desktop:

    1. There's an MCP server named "weather"
    2. To launch it by running `uv --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather run weather.py`

    Save the file, and restart **Claude for Desktop**.
  </Tab>

  <Tab title="Node">
    Let's get started with building our weather server! [You can find the complete code for what we'll be building here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/weather-server-typescript)

    ### Prerequisite knowledge

    This quickstart assumes you have familiarity with:

    * TypeScript
    * LLMs like Claude

    ### System requirements

    For TypeScript, make sure you have the latest version of Node installed.

    ### Set up your environment

    First, let's install Node.js and npm if you haven't already. You can download them from [nodejs.org](https://nodejs.org/).
    Verify your Node.js installation:

    ```bash
    node --version
    npm --version
    ```

    For this tutorial, you'll need Node.js version 16 or higher.

    Now, let's create and set up our project:

    <CodeGroup>
      ```bash MacOS/Linux
      # Create a new directory for our project
      mkdir weather
      cd weather

      # Initialize a new npm project
      npm init -y

      # Install dependencies
      npm install @modelcontextprotocol/sdk zod
      npm install -D @types/node typescript

      # Create our files
      mkdir src
      touch src/index.ts
      ```

      ```powershell Windows
      # Create a new directory for our project
      md weather
      cd weather

      # Initialize a new npm project
      npm init -y

      # Install dependencies
      npm install @modelcontextprotocol/sdk zod
      npm install -D @types/node typescript

      # Create our files
      md src
      new-item src\index.ts
      ```
    </CodeGroup>

    Update your package.json to add type: "module" and a build script:

    ```json package.json
    {
      "type": "module",
      "bin": {
        "weather": "./build/index.js"
      },
      "scripts": {
        "build": "tsc && chmod 755 build/index.js"
      },
      "files": ["build"]
    }
    ```

    Create a `tsconfig.json` in the root of your project:

    ```json tsconfig.json
    {
      "compilerOptions": {
        "target": "ES2022",
        "module": "Node16",
        "moduleResolution": "Node16",
        "outDir": "./build",
        "rootDir": "./src",
        "strict": true,
        "esModuleInterop": true,
        "skipLibCheck": true,
        "forceConsistentCasingInFileNames": true
      },
      "include": ["src/**/*"],
      "exclude": ["node_modules"]
    }
    ```

    Now let's dive into building your server.

    ## Building your server

    ### Importing packages and setting up the instance

    Add these to the top of your `src/index.ts`:

    ```typescript
    import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
    import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
    import { z } from "zod";

    const NWS_API_BASE = "https://api.weather.gov";
    const USER_AGENT = "weather-app/1.0";

    // Create server instance
    const server = new McpServer({
      name: "weather",
      version: "1.0.0",
      capabilities: {
        resources: {},
        tools: {},
      },
    });
    ```

    ### Helper functions

    Next, let's add our helper functions for querying and formatting the data from the National Weather Service API:

    ```typescript
    // Helper function for making NWS API requests
    async function makeNWSRequest<T>(url: string): Promise<T | null> {
      const headers = {
        "User-Agent": USER_AGENT,
        Accept: "application/geo+json",
      };

      try {
        const response = await fetch(url, { headers });
        if (!response.ok) {
          throw new Error(`HTTP error! status: ${response.status}`);
        }
        return (await response.json()) as T;
      } catch (error) {
        console.error("Error making NWS request:", error);
        return null;
      }
    }

    interface AlertFeature {
      properties: {
        event?: string;
        areaDesc?: string;
        severity?: string;
        status?: string;
        headline?: string;
      };
    }

    // Format alert data
    function formatAlert(feature: AlertFeature): string {
      const props = feature.properties;
      return [
        `Event: ${props.event || "Unknown"}`,
        `Area: ${props.areaDesc || "Unknown"}`,
        `Severity: ${props.severity || "Unknown"}`,
        `Status: ${props.status || "Unknown"}`,
        `Headline: ${props.headline || "No headline"}`,
        "---",
      ].join("\n");
    }

    interface ForecastPeriod {
      name?: string;
      temperature?: number;
      temperatureUnit?: string;
      windSpeed?: string;
      windDirection?: string;
      shortForecast?: string;
    }

    interface AlertsResponse {
      features: AlertFeature[];
    }

    interface PointsResponse {
      properties: {
        forecast?: string;
      };
    }

    interface ForecastResponse {
      properties: {
        periods: ForecastPeriod[];
      };
    }
    ```

    ### Implementing tool execution

    The tool execution handler is responsible for actually executing the logic of each tool. Let's add it:

    ```typescript
    // Register weather tools
    server.tool(
      "get-alerts",
      "Get weather alerts for a state",
      {
        state: z.string().length(2).describe("Two-letter state code (e.g. CA, NY)"),
      },
      async ({ state }) => {
        const stateCode = state.toUpperCase();
        const alertsUrl = `${NWS_API_BASE}/alerts?area=${stateCode}`;
        const alertsData = await makeNWSRequest<AlertsResponse>(alertsUrl);

        if (!alertsData) {
          return {
            content: [
              {
                type: "text",
                text: "Failed to retrieve alerts data",
              },
            ],
          };
        }

        const features = alertsData.features || [];
        if (features.length === 0) {
          return {
            content: [
              {
                type: "text",
                text: `No active alerts for ${stateCode}`,
              },
            ],
          };
        }

        const formattedAlerts = features.map(formatAlert);
        const alertsText = `Active alerts for ${stateCode}:\n\n${formattedAlerts.join("\n")}`;

        return {
          content: [
            {
              type: "text",
              text: alertsText,
            },
          ],
        };
      },
    );

    server.tool(
      "get-forecast",
      "Get weather forecast for a location",
      {
        latitude: z.number().min(-90).max(90).describe("Latitude of the location"),
        longitude: z
          .number()
          .min(-180)
          .max(180)
          .describe("Longitude of the location"),
      },
      async ({ latitude, longitude }) => {
        // Get grid point data
        const pointsUrl = `${NWS_API_BASE}/points/${latitude.toFixed(4)},${longitude.toFixed(4)}`;
        const pointsData = await makeNWSRequest<PointsResponse>(pointsUrl);

        if (!pointsData) {
          return {
            content: [
              {
                type: "text",
                text: `Failed to retrieve grid point data for coordinates: ${latitude}, ${longitude}. This location may not be supported by the NWS API (only US locations are supported).`,
              },
            ],
          };
        }

        const forecastUrl = pointsData.properties?.forecast;
        if (!forecastUrl) {
          return {
            content: [
              {
                type: "text",
                text: "Failed to get forecast URL from grid point data",
              },
            ],
          };
        }

        // Get forecast data
        const forecastData = await makeNWSRequest<ForecastResponse>(forecastUrl);
        if (!forecastData) {
          return {
            content: [
              {
                type: "text",
                text: "Failed to retrieve forecast data",
              },
            ],
          };
        }

        const periods = forecastData.properties?.periods || [];
        if (periods.length === 0) {
          return {
            content: [
              {
                type: "text",
                text: "No forecast periods available",
              },
            ],
          };
        }

        // Format forecast periods
        const formattedForecast = periods.map((period: ForecastPeriod) =>
          [
            `${period.name || "Unknown"}:`,
            `Temperature: ${period.temperature || "Unknown"}°${period.temperatureUnit || "F"}`,
            `Wind: ${period.windSpeed || "Unknown"} ${period.windDirection || ""}`,
            `${period.shortForecast || "No forecast available"}`,
            "---",
          ].join("\n"),
        );

        const forecastText = `Forecast for ${latitude}, ${longitude}:\n\n${formattedForecast.join("\n")}`;

        return {
          content: [
            {
              type: "text",
              text: forecastText,
            },
          ],
        };
      },
    );
    ```

    ### Running the server

    Finally, implement the main function to run the server:

    ```typescript
    async function main() {
      const transport = new StdioServerTransport();
      await server.connect(transport);
      console.error("Weather MCP Server running on stdio");
    }

    main().catch((error) => {
      console.error("Fatal error in main():", error);
      process.exit(1);
    });
    ```

    Make sure to run `npm run build` to build your server! This is a very important step in getting your server to connect.

    Let's now test your server from an existing MCP host, Claude for Desktop.

    ## Testing your server with Claude for Desktop

    <Note>
      Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/quickstart/client) tutorial to build an MCP client that connects to the server we just built.
    </Note>

    First, make sure you have Claude for Desktop installed. [You can install the latest version
    here.](https://claude.ai/download) If you already have Claude for Desktop, **make sure it's updated to the latest version.**

    We'll need to configure Claude for Desktop for whichever MCP servers you want to use. To do this, open your Claude for Desktop App configuration at `~/Library/Application Support/Claude/claude_desktop_config.json` in a text editor. Make sure to create the file if it doesn't exist.

    For example, if you have [VS Code](https://code.visualstudio.com/) installed:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```bash
        code ~/Library/Application\ Support/Claude/claude_desktop_config.json
        ```
      </Tab>

      <Tab title="Windows">
        ```powershell
        code $env:AppData\Claude\claude_desktop_config.json
        ```
      </Tab>
    </Tabs>

    You'll then add your servers in the `mcpServers` key. The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.

    In this case, we'll add our single weather server like so:

    <Tabs>
      <Tab title="MacOS/Linux">
        <CodeGroup>
          ```json Node
          {
            "mcpServers": {
              "weather": {
                "command": "node",
                "args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js"]
              }
            }
          }
          ```
        </CodeGroup>
      </Tab>

      <Tab title="Windows">
        <CodeGroup>
          ```json Node
          {
            "mcpServers": {
              "weather": {
                "command": "node",
                "args": ["C:\\PATH\\TO\\PARENT\\FOLDER\\weather\\build\\index.js"]
              }
            }
          }
          ```
        </CodeGroup>
      </Tab>
    </Tabs>

    This tells Claude for Desktop:

    1. There's an MCP server named "weather"
    2. Launch it by running `node /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/index.js`

    Save the file, and restart **Claude for Desktop**.
  </Tab>

  <Tab title="Java">
    <Note>
      This is a quickstart demo based on Spring AI MCP auto-configuration and boot starters.
      To learn how to create sync and async MCP Servers, manually, consult the [Java SDK Server](/sdk/java/mcp-server) documentation.
    </Note>

    Let's get started with building our weather server!
    [You can find the complete code for what we'll be building here.](https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/weather/starter-stdio-server)

    For more information, see the [MCP Server Boot Starter](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-server-boot-starter-docs.html) reference documentation.
    For manual MCP Server implementation, refer to the [MCP Server Java SDK documentation](/sdk/java/mcp-server).

    ### System requirements

    * Java 17 or higher installed.
    * [Spring Boot 3.3.x](https://docs.spring.io/spring-boot/installing.html) or higher

    ### Set up your environment

    Use the [Spring Initializer](https://start.spring.io/) to bootstrap the project.

    You will need to add the following dependencies:

    <Tabs>
      <Tab title="Maven">
        ```xml
        <dependencies>
              <dependency>
                  <groupId>org.springframework.ai</groupId>
                  <artifactId>spring-ai-starter-mcp-server</artifactId>
              </dependency>

              <dependency>
                  <groupId>org.springframework</groupId>
                  <artifactId>spring-web</artifactId>
              </dependency>
        </dependencies>
        ```
      </Tab>

      <Tab title="Gradle">
        ```groovy
        dependencies {
          implementation platform("org.springframework.ai:spring-ai-starter-mcp-server")
          implementation platform("org.springframework:spring-web")
        }
        ```
      </Tab>
    </Tabs>

    Then configure your application by setting the application properties:

    <CodeGroup>
      ```bash application.properties
      spring.main.bannerMode=off
      logging.pattern.console=
      ```

      ```yaml application.yml
      logging:
        pattern:
          console:
      spring:
        main:
          banner-mode: off
      ```
    </CodeGroup>

    The [Server Configuration Properties](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-server-boot-starter-docs.html#_configuration_properties) documents all available properties.

    Now let's dive into building your server.

    ## Building your server

    ### Weather Service

    Let's implement a [WeatherService.java](https://github.com/spring-projects/spring-ai-examples/blob/main/model-context-protocol/weather/starter-stdio-server/src/main/java/org/springframework/ai/mcp/sample/server/WeatherService.java) that uses a REST client to query the data from the National Weather Service API:

    ```java
    @Service
    public class WeatherService {

    	private final RestClient restClient;

    	public WeatherService() {
    		this.restClient = RestClient.builder()
    			.baseUrl("https://api.weather.gov")
    			.defaultHeader("Accept", "application/geo+json")
    			.defaultHeader("User-Agent", "WeatherApiClient/1.0 (your@email.com)")
    			.build();
    	}

      @Tool(description = "Get weather forecast for a specific latitude/longitude")
      public String getWeatherForecastByLocation(
          double latitude,   // Latitude coordinate
          double longitude   // Longitude coordinate
      ) {
          // Returns detailed forecast including:
          // - Temperature and unit
          // - Wind speed and direction
          // - Detailed forecast description
      }

      @Tool(description = "Get weather alerts for a US state")
      public String getAlerts(
          @ToolParam(description = "Two-letter US state code (e.g. CA, NY)") String state
      ) {
          // Returns active alerts including:
          // - Event type
          // - Affected area
          // - Severity
          // - Description
          // - Safety instructions
      }

      // ......
    }
    ```

    The `@Service` annotation with auto-register the service in your application context.
    The Spring AI `@Tool` annotation, making it easy to create and maintain MCP tools.

    The auto-configuration will automatically register these tools with the MCP server.

    ### Create your Boot Application

    ```java
    @SpringBootApplication
    public class McpServerApplication {

    	public static void main(String[] args) {
    		SpringApplication.run(McpServerApplication.class, args);
    	}

    	@Bean
    	public ToolCallbackProvider weatherTools(WeatherService weatherService) {
    		return  MethodToolCallbackProvider.builder().toolObjects(weatherService).build();
    	}
    }
    ```

    Uses the the `MethodToolCallbackProvider` utils to convert the `@Tools` into actionable callbacks used by the MCP server.

    ### Running the server

    Finally, let's build the server:

    ```bash
    ./mvnw clean install
    ```

    This will generate a `mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar` file within the `target` folder.

    Let's now test your server from an existing MCP host, Claude for Desktop.

    ## Testing your server with Claude for Desktop

    <Note>
      Claude for Desktop is not yet available on Linux.
    </Note>

    First, make sure you have Claude for Desktop installed.
    [You can install the latest version here.](https://claude.ai/download) If you already have Claude for Desktop, **make sure it's updated to the latest version.**

    We'll need to configure Claude for Desktop for whichever MCP servers you want to use.
    To do this, open your Claude for Desktop App configuration at `~/Library/Application Support/Claude/claude_desktop_config.json` in a text editor.
    Make sure to create the file if it doesn't exist.

    For example, if you have [VS Code](https://code.visualstudio.com/) installed:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```bash
        code ~/Library/Application\ Support/Claude/claude_desktop_config.json
        ```
      </Tab>

      <Tab title="Windows">
        ```powershell
        code $env:AppData\Claude\claude_desktop_config.json
        ```
      </Tab>
    </Tabs>

    You'll then add your servers in the `mcpServers` key.
    The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.

    In this case, we'll add our single weather server like so:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```json java
        {
          "mcpServers": {
            "spring-ai-mcp-weather": {
              "command": "java",
              "args": [
                "-Dspring.ai.mcp.server.stdio=true",
                "-jar",
                "/ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar"
              ]
            }
          }
        }
        ```
      </Tab>

      <Tab title="Windows">
        ```json java
        {
          "mcpServers": {
            "spring-ai-mcp-weather": {
              "command": "java",
              "args": [
                "-Dspring.ai.mcp.server.transport=STDIO",
                "-jar",
                "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\weather\\mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar"
              ]
            }
          }
        }
        ```
      </Tab>
    </Tabs>

    <Note>
      Make sure you pass in the absolute path to your server.
    </Note>

    This tells Claude for Desktop:

    1. There's an MCP server named "my-weather-server"
    2. To launch it by running `java -jar /ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar`

    Save the file, and restart **Claude for Desktop**.

    ## Testing your server with Java client

    ### Create a MCP Client manually

    Use the `McpClient` to connect to the server:

    ```java
    var stdioParams = ServerParameters.builder("java")
      .args("-jar", "/ABSOLUTE/PATH/TO/PARENT/FOLDER/mcp-weather-stdio-server-0.0.1-SNAPSHOT.jar")
      .build();

    var stdioTransport = new StdioClientTransport(stdioParams);

    var mcpClient = McpClient.sync(stdioTransport).build();

    mcpClient.initialize();

    ListToolsResult toolsList = mcpClient.listTools();

    CallToolResult weather = mcpClient.callTool(
      new CallToolRequest("getWeatherForecastByLocation",
          Map.of("latitude", "47.6062", "longitude", "-122.3321")));

    CallToolResult alert = mcpClient.callTool(
      new CallToolRequest("getAlerts", Map.of("state", "NY")));

    mcpClient.closeGracefully();
    ```

    ### Use MCP Client Boot Starter

    Create a new boot starter application using the `spring-ai-starter-mcp-client` dependency:

    ```xml
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-mcp-client</artifactId>
    </dependency>
    ```

    and set the `spring.ai.mcp.client.stdio.servers-configuration` property to point to your `claude_desktop_config.json`.
    You can reuse the existing Anthropic Desktop configuration:

    ```properties
    spring.ai.mcp.client.stdio.servers-configuration=file:PATH/TO/claude_desktop_config.json
    ```

    When you start your client application, the auto-configuration will create, automatically MCP clients from the claude\_desktop\_config.json.

    For more information, see the [MCP Client Boot Starters](https://docs.spring.io/spring-ai/reference/api/mcp/mcp-server-boot-client-docs.html) reference documentation.

    ## More Java MCP Server examples

    The [starter-webflux-server](https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/weather/starter-webflux-server) demonstrates how to create a MCP server using SSE transport.
    It showcases how to define and register MCP Tools, Resources, and Prompts, using the Spring Boot's auto-configuration capabilities.
  </Tab>

  <Tab title="Kotlin">
    Let's get started with building our weather server! [You can find the complete code for what we'll be building here.](https://github.com/modelcontextprotocol/kotlin-sdk/tree/main/samples/weather-stdio-server)

    ### Prerequisite knowledge

    This quickstart assumes you have familiarity with:

    * Kotlin
    * LLMs like Claude

    ### System requirements

    * Java 17 or higher installed.

    ### Set up your environment

    First, let's install `java` and `gradle` if you haven't already.
    You can download `java` from [official Oracle JDK website](https://www.oracle.com/java/technologies/downloads/).
    Verify your `java` installation:

    ```bash
    java --version
    ```

    Now, let's create and set up your project:

    <CodeGroup>
      ```bash MacOS/Linux
      # Create a new directory for our project
      mkdir weather
      cd weather

      # Initialize a new kotlin project
      gradle init
      ```

      ```powershell Windows
      # Create a new directory for our project
      md weather
      cd weather

      # Initialize a new kotlin project
      gradle init
      ```
    </CodeGroup>

    After running `gradle init`, you will be presented with options for creating your project.
    Select **Application** as the project type, **Kotlin** as the programming language, and **Java 17** as the Java version.

    Alternatively, you can create a Kotlin application using the [IntelliJ IDEA project wizard](https://kotlinlang.org/docs/jvm-get-started.html).

    After creating the project, add the following dependencies:

    <CodeGroup>
      ```kotlin build.gradle.kts
      val mcpVersion = "0.4.0"
      val slf4jVersion = "2.0.9"
      val ktorVersion = "3.1.1"

      dependencies {
          implementation("io.modelcontextprotocol:kotlin-sdk:$mcpVersion")
          implementation("org.slf4j:slf4j-nop:$slf4jVersion")
          implementation("io.ktor:ktor-client-content-negotiation:$ktorVersion")
          implementation("io.ktor:ktor-serialization-kotlinx-json:$ktorVersion")
      }
      ```

      ```groovy build.gradle
      def mcpVersion = '0.3.0'
      def slf4jVersion = '2.0.9'
      def ktorVersion = '3.1.1'

      dependencies {
          implementation "io.modelcontextprotocol:kotlin-sdk:$mcpVersion"
          implementation "org.slf4j:slf4j-nop:$slf4jVersion"
          implementation "io.ktor:ktor-client-content-negotiation:$ktorVersion"
          implementation "io.ktor:ktor-serialization-kotlinx-json:$ktorVersion"
      }
      ```
    </CodeGroup>

    Also, add the following plugins to your build script:

    <CodeGroup>
      ```kotlin build.gradle.kts
      plugins {
          kotlin("plugin.serialization") version "your_version_of_kotlin"
          id("com.github.johnrengelman.shadow") version "8.1.1"
      }
      ```

      ```groovy build.gradle
      plugins {
          id 'org.jetbrains.kotlin.plugin.serialization' version 'your_version_of_kotlin'
          id 'com.github.johnrengelman.shadow' version '8.1.1'
      }
      ```
    </CodeGroup>

    Now let’s dive into building your server.

    ## Building your server

    ### Setting up the instance

    Add a server initialization function:

    ```kotlin
    // Main function to run the MCP server
    fun `run mcp server`() {
        // Create the MCP Server instance with a basic implementation
        val server = Server(
            Implementation(
                name = "weather", // Tool name is "weather"
                version = "1.0.0" // Version of the implementation
            ),
            ServerOptions(
                capabilities = ServerCapabilities(tools = ServerCapabilities.Tools(listChanged = true))
            )
        )

        // Create a transport using standard IO for server communication
        val transport = StdioServerTransport(
            System.`in`.asInput(),
            System.out.asSink().buffered()
        )

        runBlocking {
            server.connect(transport)
            val done = Job()
            server.onClose {
                done.complete()
            }
            done.join()
        }
    }
    ```

    ### Weather API helper functions

    Next, let's add functions and data classes for querying and converting responses from the National Weather Service API:

    ```kotlin
    // Extension function to fetch forecast information for given latitude and longitude
    suspend fun HttpClient.getForecast(latitude: Double, longitude: Double): List<String> {
        val points = this.get("/points/$latitude,$longitude").body<Points>()
        val forecast = this.get(points.properties.forecast).body<Forecast>()
        return forecast.properties.periods.map { period ->
            """
                ${period.name}:
                Temperature: ${period.temperature} ${period.temperatureUnit}
                Wind: ${period.windSpeed} ${period.windDirection}
                Forecast: ${period.detailedForecast}
            """.trimIndent()
        }
    }

    // Extension function to fetch weather alerts for a given state
    suspend fun HttpClient.getAlerts(state: String): List<String> {
        val alerts = this.get("/alerts/active/area/$state").body<Alert>()
        return alerts.features.map { feature ->
            """
                Event: ${feature.properties.event}
                Area: ${feature.properties.areaDesc}
                Severity: ${feature.properties.severity}
                Description: ${feature.properties.description}
                Instruction: ${feature.properties.instruction}
            """.trimIndent()
        }
    }

    @Serializable
    data class Points(
        val properties: Properties
    ) {
        @Serializable
        data class Properties(val forecast: String)
    }

    @Serializable
    data class Forecast(
        val properties: Properties
    ) {
        @Serializable
        data class Properties(val periods: List<Period>)

        @Serializable
        data class Period(
            val number: Int, val name: String, val startTime: String, val endTime: String,
            val isDaytime: Boolean, val temperature: Int, val temperatureUnit: String,
            val temperatureTrend: String, val probabilityOfPrecipitation: JsonObject,
            val windSpeed: String, val windDirection: String,
            val shortForecast: String, val detailedForecast: String,
        )
    }

    @Serializable
    data class Alert(
        val features: List<Feature>
    ) {
        @Serializable
        data class Feature(
            val properties: Properties
        )

        @Serializable
        data class Properties(
            val event: String, val areaDesc: String, val severity: String,
            val description: String, val instruction: String?,
        )
    }
    ```

    ### Implementing tool execution

    The tool execution handler is responsible for actually executing the logic of each tool. Let's add it:

    ```kotlin
    // Create an HTTP client with a default request configuration and JSON content negotiation
    val httpClient = HttpClient {
        defaultRequest {
            url("https://api.weather.gov")
            headers {
                append("Accept", "application/geo+json")
                append("User-Agent", "WeatherApiClient/1.0")
            }
            contentType(ContentType.Application.Json)
        }
        // Install content negotiation plugin for JSON serialization/deserialization
        install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true }) }
    }

    // Register a tool to fetch weather alerts by state
    server.addTool(
        name = "get_alerts",
        description = """
            Get weather alerts for a US state. Input is Two-letter US state code (e.g. CA, NY)
        """.trimIndent(),
        inputSchema = Tool.Input(
            properties = buildJsonObject {
                putJsonObject("state") {
                    put("type", "string")
                    put("description", "Two-letter US state code (e.g. CA, NY)")
                }
            },
            required = listOf("state")
        )
    ) { request ->
        val state = request.arguments["state"]?.jsonPrimitive?.content
        if (state == null) {
            return@addTool CallToolResult(
                content = listOf(TextContent("The 'state' parameter is required."))
            )
        }

        val alerts = httpClient.getAlerts(state)

        CallToolResult(content = alerts.map { TextContent(it) })
    }

    // Register a tool to fetch weather forecast by latitude and longitude
    server.addTool(
        name = "get_forecast",
        description = """
            Get weather forecast for a specific latitude/longitude
        """.trimIndent(),
        inputSchema = Tool.Input(
            properties = buildJsonObject {
                putJsonObject("latitude") { put("type", "number") }
                putJsonObject("longitude") { put("type", "number") }
            },
            required = listOf("latitude", "longitude")
        )
    ) { request ->
        val latitude = request.arguments["latitude"]?.jsonPrimitive?.doubleOrNull
        val longitude = request.arguments["longitude"]?.jsonPrimitive?.doubleOrNull
        if (latitude == null || longitude == null) {
            return@addTool CallToolResult(
                content = listOf(TextContent("The 'latitude' and 'longitude' parameters are required."))
            )
        }

        val forecast = httpClient.getForecast(latitude, longitude)

        CallToolResult(content = forecast.map { TextContent(it) })
    }
    ```

    ### Running the server

    Finally, implement the main function to run the server:

    ```kotlin
    fun main() = `run mcp server`()
    ```

    Make sure to run `./gradlew build` to build your server. This is a very important step in getting your server to connect.

    Let's now test your server from an existing MCP host, Claude for Desktop.

    ## Testing your server with Claude for Desktop

    <Note>
      Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/quickstart/client) tutorial to build an MCP client that connects to the server we just built.
    </Note>

    First, make sure you have Claude for Desktop installed. [You can install the latest version
    here.](https://claude.ai/download) If you already have Claude for Desktop, **make sure it's updated to the latest version.**

    We'll need to configure Claude for Desktop for whichever MCP servers you want to use.
    To do this, open your Claude for Desktop App configuration at `~/Library/Application Support/Claude/claude_desktop_config.json` in a text editor.
    Make sure to create the file if it doesn't exist.

    For example, if you have [VS Code](https://code.visualstudio.com/) installed:

    <CodeGroup>
      ```bash MacOS/Linux
      code ~/Library/Application\ Support/Claude/claude_desktop_config.json
      ```

      ```powershell Windows
      code $env:AppData\Claude\claude_desktop_config.json
      ```
    </CodeGroup>

    You'll then add your servers in the `mcpServers` key.
    The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.

    In this case, we'll add our single weather server like so:

    <CodeGroup>
      ```json MacOS/Linux
      {
        "mcpServers": {
          "weather": {
            "command": "java",
            "args": [
              "-jar",
              "/ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/libs/weather-0.1.0-all.jar"
            ]
          }
        }
      }
      ```

      ```json Windows
      {
        "mcpServers": {
          "weather": {
            "command": "java",
            "args": [
              "-jar",
              "C:\\PATH\\TO\\PARENT\\FOLDER\\weather\\build\\libs\\weather-0.1.0-all.jar"
            ]
          }
        }
      }
      ```
    </CodeGroup>

    This tells Claude for Desktop:

    1. There's an MCP server named "weather"
    2. Launch it by running `java -jar /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather/build/libs/weather-0.1.0-all.jar`

    Save the file, and restart **Claude for Desktop**.
  </Tab>

  <Tab title="C#">
    Let's get started with building our weather server! [You can find the complete code for what we'll be building here.](https://github.com/modelcontextprotocol/csharp-sdk/tree/main/samples/QuickstartWeatherServer)

    ### Prerequisite knowledge

    This quickstart assumes you have familiarity with:

    * C#
    * LLMs like Claude
    * .NET 8 or higher

    ### System requirements

    * [.NET 8 SDK](https://dotnet.microsoft.com/download/dotnet/8.0) or higher installed.

    ### Set up your environment

    First, let's install `dotnet` if you haven't already. You can download `dotnet` from [official Microsoft .NET website](https://dotnet.microsoft.com/download/). Verify your `dotnet` installation:

    ```bash
    dotnet --version
    ```

    Now, let's create and set up your project:

    <CodeGroup>
      ```bash MacOS/Linux
      # Create a new directory for our project
      mkdir weather
      cd weather
      # Initialize a new C# project
      dotnet new console
      ```

      ```powershell Windows
      # Create a new directory for our project
      mkdir weather
      cd weather
      # Initialize a new C# project
      dotnet new console
      ```
    </CodeGroup>

    After running `dotnet new console`, you will be presented with a new C# project.
    You can open the project in your favorite IDE, such as [Visual Studio](https://visualstudio.microsoft.com/) or [Rider](https://www.jetbrains.com/rider/).
    Alternatively, you can create a C# application using the [Visual Studio project wizard](https://learn.microsoft.com/en-us/visualstudio/get-started/csharp/tutorial-console?view=vs-2022).
    After creating the project, add NuGet package for the Model Context Protocol SDK and hosting:

    ```bash
    # Add the Model Context Protocol SDK NuGet package
    dotnet add package ModelContextProtocol --prerelease
    # Add the .NET Hosting NuGet package
    dotnet add package Microsoft.Extensions.Hosting
    ```

    Now let’s dive into building your server.

    ## Building your server

    Open the `Program.cs` file in your project and replace its contents with the following code:

    ```csharp
    using Microsoft.Extensions.DependencyInjection;
    using Microsoft.Extensions.Hosting;
    using ModelContextProtocol;
    using System.Net.Http.Headers;

    var builder = Host.CreateEmptyApplicationBuilder(settings: null);

    builder.Services.AddMcpServer()
        .WithStdioServerTransport()
        .WithToolsFromAssembly();

    builder.Services.AddSingleton(_ =>
    {
        var client = new HttpClient() { BaseAddress = new Uri("https://api.weather.gov") };
        client.DefaultRequestHeaders.UserAgent.Add(new ProductInfoHeaderValue("weather-tool", "1.0"));
        return client;
    });

    var app = builder.Build();

    await app.RunAsync();
    ```

    <Note>
      When creating the `ApplicationHostBuilder`, ensure you use `CreateEmptyApplicationBuilder` instead of `CreateDefaultBuilder`. This ensures that the server does not write any additional messages to the console. This is only necessary for servers using STDIO transport.
    </Note>

    This code sets up a basic console application that uses the Model Context Protocol SDK to create an MCP server with standard I/O transport.

    ### Weather API helper functions

    Create an extension class for `HttpClient` which helps simplify JSON request handling:

    ```csharp
    using System.Text.Json;

    internal static class HttpClientExt
    {
        public static async Task<JsonDocument> ReadJsonDocumentAsync(this HttpClient client, string requestUri)
        {
            using var response = await client.GetAsync(requestUri);
            response.EnsureSuccessStatusCode();
            return await JsonDocument.ParseAsync(await response.Content.ReadAsStreamAsync());
        }
    }
    ```

    Next, define a class with the tool execution handlers for querying and converting responses from the National Weather Service API:

    ```csharp
    using ModelContextProtocol.Server;
    using System.ComponentModel;
    using System.Globalization;
    using System.Text.Json;

    namespace QuickstartWeatherServer.Tools;

    [McpServerToolType]
    public static class WeatherTools
    {
        [McpServerTool, Description("Get weather alerts for a US state.")]
        public static async Task<string> GetAlerts(
            HttpClient client,
            [Description("The US state to get alerts for.")] string state)
        {
            using var jsonDocument = await client.ReadJsonDocumentAsync($"/alerts/active/area/{state}");
            var jsonElement = jsonDocument.RootElement;
            var alerts = jsonElement.GetProperty("features").EnumerateArray();

            if (!alerts.Any())
            {
                return "No active alerts for this state.";
            }

            return string.Join("\n--\n", alerts.Select(alert =>
            {
                JsonElement properties = alert.GetProperty("properties");
                return $"""
                        Event: {properties.GetProperty("event").GetString()}
                        Area: {properties.GetProperty("areaDesc").GetString()}
                        Severity: {properties.GetProperty("severity").GetString()}
                        Description: {properties.GetProperty("description").GetString()}
                        Instruction: {properties.GetProperty("instruction").GetString()}
                        """;
            }));
        }

        [McpServerTool, Description("Get weather forecast for a location.")]
        public static async Task<string> GetForecast(
            HttpClient client,
            [Description("Latitude of the location.")] double latitude,
            [Description("Longitude of the location.")] double longitude)
        {
            var pointUrl = string.Create(CultureInfo.InvariantCulture, $"/points/{latitude},{longitude}");
            using var jsonDocument = await client.ReadJsonDocumentAsync(pointUrl);
            var forecastUrl = jsonDocument.RootElement.GetProperty("properties").GetProperty("forecast").GetString()
                ?? throw new Exception($"No forecast URL provided by {client.BaseAddress}points/{latitude},{longitude}");

            using var forecastDocument = await client.ReadJsonDocumentAsync(forecastUrl);
            var periods = forecastDocument.RootElement.GetProperty("properties").GetProperty("periods").EnumerateArray();

            return string.Join("\n---\n", periods.Select(period => $"""
                    {period.GetProperty("name").GetString()}
                    Temperature: {period.GetProperty("temperature").GetInt32()}°F
                    Wind: {period.GetProperty("windSpeed").GetString()} {period.GetProperty("windDirection").GetString()}
                    Forecast: {period.GetProperty("detailedForecast").GetString()}
                    """));
        }
    }
    ```

    ### Running the server

    Finally, run the server using the following command:

    ```bash
    dotnet run
    ```

    This will start the server and listen for incoming requests on standard input/output.

    ## Testing your server with Claude for Desktop

    <Note>
      Claude for Desktop is not yet available on Linux. Linux users can proceed to the [Building a client](/quickstart/client) tutorial to build an MCP client that connects to the server we just built.
    </Note>

    First, make sure you have Claude for Desktop installed. [You can install the latest version
    here.](https://claude.ai/download) If you already have Claude for Desktop, **make sure it's updated to the latest version.**
    We'll need to configure Claude for Desktop for whichever MCP servers you want to use. To do this, open your Claude for Desktop App configuration at `~/Library/Application Support/Claude/claude_desktop_config.json` in a text editor. Make sure to create the file if it doesn't exist.
    For example, if you have [VS Code](https://code.visualstudio.com/) installed:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```bash
        code ~/Library/Application\ Support/Claude/claude_desktop_config.json
        ```
      </Tab>

      <Tab title="Windows">
        ```powershell
        code $env:AppData\Claude\claude_desktop_config.json
        ```
      </Tab>
    </Tabs>

    You'll then add your servers in the `mcpServers` key. The MCP UI elements will only show up in Claude for Desktop if at least one server is properly configured.
    In this case, we'll add our single weather server like so:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```json
        {
          "mcpServers": {
            "weather": {
              "command": "dotnet",
              "args": ["run", "--project", "/ABSOLUTE/PATH/TO/PROJECT", "--no-build"]
            }
          }
        }
        ```
      </Tab>

      <Tab title="Windows">
        ```json
        {
          "mcpServers": {
            "weather": {
              "command": "dotnet",
              "args": [
                "run",
                "--project",
                "C:\\ABSOLUTE\\PATH\\TO\\PROJECT",
                "--no-build"
              ]
            }
          }
        }
        ```
      </Tab>
    </Tabs>

    This tells Claude for Desktop:

    1. There's an MCP server named "weather"
    2. Launch it by running `dotnet run /ABSOLUTE/PATH/TO/PROJECT`
       Save the file, and restart **Claude for Desktop**.
  </Tab>
</Tabs>

### Test with commands

Let's make sure Claude for Desktop is picking up the two tools we've exposed in our `weather` server. You can do this by looking for the "Search and tools" <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/claude-desktop-mcp-slider.svg" style={{display: 'inline', margin: 0, height: '1.3em'}} /> icon:

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/visual-indicator-mcp-tools.png" />
</Frame>

After clicking on the slider icon, you should see two tools listed:

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/available-mcp-tools.png" />
</Frame>

If your server isn't being picked up by Claude for Desktop, proceed to the [Troubleshooting](#troubleshooting) section for debugging tips.

If the tool settings icon has shown up, you can now test your server by running the following commands in Claude for Desktop:

* What's the weather in Sacramento?
* What are the active weather alerts in Texas?

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/current-weather.png" />
</Frame>

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/weather-alerts.png" />
</Frame>

<Note>
  Since this is the US National Weather service, the queries will only work for US locations.
</Note>

## What's happening under the hood

When you ask a question:

1. The client sends your question to Claude
2. Claude analyzes the available tools and decides which one(s) to use
3. The client executes the chosen tool(s) through the MCP server
4. The results are sent back to Claude
5. Claude formulates a natural language response
6. The response is displayed to you!

## Troubleshooting

<AccordionGroup>
  <Accordion title="Claude for Desktop Integration Issues">
    **Getting logs from Claude for Desktop**

    Claude.app logging related to MCP is written to log files in `~/Library/Logs/Claude`:

    * `mcp.log` will contain general logging about MCP connections and connection failures.
    * Files named `mcp-server-SERVERNAME.log` will contain error (stderr) logging from the named server.

    You can run the following command to list recent logs and follow along with any new ones:

    ```bash
    # Check Claude's logs for errors
    tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
    ```

    **Server not showing up in Claude**

    1. Check your `claude_desktop_config.json` file syntax
    2. Make sure the path to your project is absolute and not relative
    3. Restart Claude for Desktop completely

    **Tool calls failing silently**

    If Claude attempts to use the tools but they fail:

    1. Check Claude's logs for errors
    2. Verify your server builds and runs without errors
    3. Try restarting Claude for Desktop

    **None of this is working. What do I do?**

    Please refer to our [debugging guide](/docs/tools/debugging) for better debugging tools and more detailed guidance.
  </Accordion>

  <Accordion title="Weather API Issues">
    **Error: Failed to retrieve grid point data**

    This usually means either:

    1. The coordinates are outside the US
    2. The NWS API is having issues
    3. You're being rate limited

    Fix:

    * Verify you're using US coordinates
    * Add a small delay between requests
    * Check the NWS API status page

    **Error: No active alerts for \[STATE]**

    This isn't an error - it just means there are no current weather alerts for that state. Try a different state or check during severe weather.
  </Accordion>
</AccordionGroup>

<Note>
  For more advanced troubleshooting, check out our guide on [Debugging MCP](/docs/tools/debugging)
</Note>

## Next steps

<CardGroup cols={2}>
  <Card title="Building a client" icon="outlet" href="/quickstart/client">
    Learn how to build your own MCP client that can connect to your server
  </Card>

  <Card title="Example servers" icon="grid" href="/examples">
    Check out our gallery of official MCP servers and implementations
  </Card>

  <Card title="Debugging Guide" icon="bug" href="/docs/tools/debugging">
    Learn how to effectively debug MCP servers and integrations
  </Card>

  <Card title="Building MCP with LLMs" icon="comments" href="/tutorials/building-mcp-with-llms">
    Learn how to use LLMs like Claude to speed up your MCP development
  </Card>
</CardGroup>


# For Claude Desktop Users
Source: https://modelcontextprotocol.io/quickstart/user

Get started using pre-built servers in Claude for Desktop.

In this tutorial, you will extend [Claude for Desktop](https://claude.ai/download) so that it can read from your computer's file system, write new files, move files, and even search files.

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-filesystem.png" />
</Frame>

Don't worry — it will ask you for your permission before executing these actions!

## 1. Download Claude for Desktop

Start by downloading [Claude for Desktop](https://claude.ai/download), choosing either macOS or Windows. (Linux is not yet supported for Claude for Desktop.)

Follow the installation instructions.

If you already have Claude for Desktop, make sure it's on the latest version by clicking on the Claude menu on your computer and selecting "Check for Updates..."

## 2. Add the Filesystem MCP Server

To add this filesystem functionality, we will be installing a pre-built [Filesystem MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem) to Claude for Desktop. This is one of several current [reference servers](https://github.com/modelcontextprotocol/servers/tree/main) and many community-created servers.

Get started by opening up the Claude menu on your computer and select "Settings..." Please note that these are not the Claude Account Settings found in the app window itself.

This is what it should look like on a Mac:

<Frame style={{ textAlign: "center" }}>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-menu.png" width="400" />
</Frame>

Click on "Developer" in the left-hand bar of the Settings pane, and then click on "Edit Config":

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-developer.png" />
</Frame>

This will create a configuration file at:

* macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
* Windows: `%APPDATA%\Claude\claude_desktop_config.json`

if you don't already have one, and will display the file in your file system.

Open up the configuration file in any text editor. Replace the file contents with this:

<Tabs>
  <Tab title="MacOS/Linux">
    ```json
    {
      "mcpServers": {
        "filesystem": {
          "command": "npx",
          "args": [
            "-y",
            "@modelcontextprotocol/server-filesystem",
            "/Users/username/Desktop",
            "/Users/username/Downloads"
          ]
        }
      }
    }
    ```
  </Tab>

  <Tab title="Windows">
    ```json
    {
      "mcpServers": {
        "filesystem": {
          "command": "npx",
          "args": [
            "-y",
            "@modelcontextprotocol/server-filesystem",
            "C:\\Users\\username\\Desktop",
            "C:\\Users\\username\\Downloads"
          ]
        }
      }
    }
    ```
  </Tab>
</Tabs>

Make sure to replace `username` with your computer's username. The paths should point to valid directories that you want Claude to be able to access and modify. It's set up to work for Desktop and Downloads, but you can add more paths as well.

You will also need [Node.js](https://nodejs.org) on your computer for this to run properly. To verify you have Node installed, open the command line on your computer.

* On macOS, open the Terminal from your Applications folder
* On Windows, press Windows + R, type "cmd", and press Enter

Once in the command line, verify you have Node installed by entering in the following command:

```bash
node --version
```

If you get an error saying "command not found" or "node is not recognized", download Node from [nodejs.org](https://nodejs.org/).

<Tip>
  **How does the configuration file work?**

  This configuration file tells Claude for Desktop which MCP servers to start up every time you start the application. In this case, we have added one server called "filesystem" that will use the Node `npx` command to install and run `@modelcontextprotocol/server-filesystem`. This server, described [here](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem), will let you access your file system in Claude for Desktop.
</Tip>

<Warning>
  **Command Privileges**

  Claude for Desktop will run the commands in the configuration file with the permissions of your user account, and access to your local files. Only add commands if you understand and trust the source.
</Warning>

## 3. Restart Claude

After updating your configuration file, you need to restart Claude for Desktop.

Upon restarting, you should see a slider <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/claude-desktop-mcp-slider.svg" style={{display: 'inline', margin: 0, height: '1.3em'}} /> icon in the bottom left corner of the input box:

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-slider.png" />
</Frame>

After clicking on the slider icon, you should see the tools that come with the Filesystem MCP Server:

<Frame style={{ textAlign: "center" }}>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-tools.png" width="400" />
</Frame>

If your server isn't being picked up by Claude for Desktop, proceed to the [Troubleshooting](#troubleshooting) section for debugging tips.

## 4. Try it out!

You can now talk to Claude and ask it about your filesystem. It should know when to call the relevant tools.

Things you might try asking Claude:

* Can you write a poem and save it to my desktop?
* What are some work-related files in my downloads folder?
* Can you take all the images on my desktop and move them to a new folder called "Images"?

As needed, Claude will call the relevant tools and seek your approval before taking an action:

<Frame style={{ textAlign: "center" }}>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-approve.png" width="500" />
</Frame>

## Troubleshooting

<AccordionGroup>
  <Accordion title="Server not showing up in Claude / hammer icon missing">
    1. Restart Claude for Desktop completely
    2. Check your `claude_desktop_config.json` file syntax
    3. Make sure the file paths included in `claude_desktop_config.json` are valid and that they are absolute and not relative
    4. Look at [logs](#getting-logs-from-claude-for-desktop) to see why the server is not connecting
    5. In your command line, try manually running the server (replacing `username` as you did in `claude_desktop_config.json`) to see if you get any errors:

    <Tabs>
      <Tab title="MacOS/Linux">
        ```bash
        npx -y @modelcontextprotocol/server-filesystem /Users/username/Desktop /Users/username/Downloads
        ```
      </Tab>

      <Tab title="Windows">
        ```bash
        npx -y @modelcontextprotocol/server-filesystem C:\Users\username\Desktop C:\Users\username\Downloads
        ```
      </Tab>
    </Tabs>
  </Accordion>

  <Accordion title="Getting logs from Claude for Desktop">
    Claude.app logging related to MCP is written to log files in:

    * macOS: `~/Library/Logs/Claude`

    * Windows: `%APPDATA%\Claude\logs`

    * `mcp.log` will contain general logging about MCP connections and connection failures.

    * Files named `mcp-server-SERVERNAME.log` will contain error (stderr) logging from the named server.

    You can run the following command to list recent logs and follow along with any new ones (on Windows, it will only show recent logs):

    <Tabs>
      <Tab title="MacOS/Linux">
        ```bash
        # Check Claude's logs for errors
        tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
        ```
      </Tab>

      <Tab title="Windows">
        ```bash
        type "%APPDATA%\Claude\logs\mcp*.log"
        ```
      </Tab>
    </Tabs>
  </Accordion>

  <Accordion title="Tool calls failing silently">
    If Claude attempts to use the tools but they fail:

    1. Check Claude's logs for errors
    2. Verify your server builds and runs without errors
    3. Try restarting Claude for Desktop
  </Accordion>

  <Accordion title="None of this is working. What do I do?">
    Please refer to our [debugging guide](/docs/tools/debugging) for better debugging tools and more detailed guidance.
  </Accordion>

  <Accordion title="ENOENT error and `${APPDATA}` in paths on Windows">
    If your configured server fails to load, and you see within its logs an error referring to `${APPDATA}` within a path, you may need to add the expanded value of `%APPDATA%` to your `env` key in `claude_desktop_config.json`:

    ```json
    {
      "brave-search": {
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-brave-search"],
        "env": {
          "APPDATA": "C:\\Users\\user\\AppData\\Roaming\\",
          "BRAVE_API_KEY": "..."
        }
      }
    }
    ```

    With this change in place, launch Claude Desktop once again.

    <Warning>
      **NPM should be installed globally**

      The `npx` command may continue to fail if you have not installed NPM globally. If NPM is already installed globally, you will find `%APPDATA%\npm` exists on your system. If not, you can install NPM globally by running the following command:

      ```bash
      npm install -g npm
      ```
    </Warning>
  </Accordion>
</AccordionGroup>

## Next steps

<CardGroup cols={2}>
  <Card title="Explore other servers" icon="grid" href="/examples">
    Check out our gallery of official MCP servers and implementations
  </Card>

  <Card title="Build your own server" icon="code" href="/quickstart/server">
    Now build your own custom server to use in Claude for Desktop and other
    clients
  </Card>
</CardGroup>


# Architecture
Source: https://modelcontextprotocol.io/specification/2025-06-18/architecture/index



<div id="enable-section-numbers" />

The Model Context Protocol (MCP) follows a client-host-server architecture where each
host can run multiple client instances. This architecture enables users to integrate AI
capabilities across applications while maintaining clear security boundaries and
isolating concerns. Built on JSON-RPC, MCP provides a stateful session protocol focused
on context exchange and sampling coordination between clients and servers.

## Core Components

```mermaid
graph LR
    subgraph "Application Host Process"
        H[Host]
        C1[Client 1]
        C2[Client 2]
        C3[Client 3]
        H --> C1
        H --> C2
        H --> C3
    end

    subgraph "Local machine"
        S1[Server 1<br>Files & Git]
        S2[Server 2<br>Database]
        R1[("Local<br>Resource A")]
        R2[("Local<br>Resource B")]

        C1 --> S1
        C2 --> S2
        S1 <--> R1
        S2 <--> R2
    end

    subgraph "Internet"
        S3[Server 3<br>External APIs]
        R3[("Remote<br>Resource C")]

        C3 --> S3
        S3 <--> R3
    end
```

### Host

The host process acts as the container and coordinator:

* Creates and manages multiple client instances
* Controls client connection permissions and lifecycle
* Enforces security policies and consent requirements
* Handles user authorization decisions
* Coordinates AI/LLM integration and sampling
* Manages context aggregation across clients

### Clients

Each client is created by the host and maintains an isolated server connection:

* Establishes one stateful session per server
* Handles protocol negotiation and capability exchange
* Routes protocol messages bidirectionally
* Manages subscriptions and notifications
* Maintains security boundaries between servers

A host application creates and manages multiple clients, with each client having a 1:1
relationship with a particular server.

### Servers

Servers provide specialized context and capabilities:

* Expose resources, tools and prompts via MCP primitives
* Operate independently with focused responsibilities
* Request sampling through client interfaces
* Must respect security constraints
* Can be local processes or remote services

## Design Principles

MCP is built on several key design principles that inform its architecture and
implementation:

1. **Servers should be extremely easy to build**

   * Host applications handle complex orchestration responsibilities
   * Servers focus on specific, well-defined capabilities
   * Simple interfaces minimize implementation overhead
   * Clear separation enables maintainable code

2. **Servers should be highly composable**

   * Each server provides focused functionality in isolation
   * Multiple servers can be combined seamlessly
   * Shared protocol enables interoperability
   * Modular design supports extensibility

3. **Servers should not be able to read the whole conversation, nor "see into" other
   servers**

   * Servers receive only necessary contextual information
   * Full conversation history stays with the host
   * Each server connection maintains isolation
   * Cross-server interactions are controlled by the host
   * Host process enforces security boundaries

4. **Features can be added to servers and clients progressively**
   * Core protocol provides minimal required functionality
   * Additional capabilities can be negotiated as needed
   * Servers and clients evolve independently
   * Protocol designed for future extensibility
   * Backwards compatibility is maintained

## Capability Negotiation

The Model Context Protocol uses a capability-based negotiation system where clients and
servers explicitly declare their supported features during initialization. Capabilities
determine which protocol features and primitives are available during a session.

* Servers declare capabilities like resource subscriptions, tool support, and prompt
  templates
* Clients declare capabilities like sampling support and notification handling
* Both parties must respect declared capabilities throughout the session
* Additional capabilities can be negotiated through extensions to the protocol

```mermaid
sequenceDiagram
    participant Host
    participant Client
    participant Server

    Host->>+Client: Initialize client
    Client->>+Server: Initialize session with capabilities
    Server-->>Client: Respond with supported capabilities

    Note over Host,Server: Active Session with Negotiated Features

    loop Client Requests
        Host->>Client: User- or model-initiated action
        Client->>Server: Request (tools/resources)
        Server-->>Client: Response
        Client-->>Host: Update UI or respond to model
    end

    loop Server Requests
        Server->>Client: Request (sampling)
        Client->>Host: Forward to AI
        Host-->>Client: AI response
        Client-->>Server: Response
    end

    loop Notifications
        Server--)Client: Resource updates
        Client--)Server: Status changes
    end

    Host->>Client: Terminate
    Client->>-Server: End session
    deactivate Server
```

Each capability unlocks specific protocol features for use during the session. For
example:

* Implemented [server features](/specification/2025-06-18/server) must be advertised in the
  server's capabilities
* Emitting resource subscription notifications requires the server to declare
  subscription support
* Tool invocation requires the server to declare tool capabilities
* [Sampling](/specification/2025-06-18/client) requires the client to declare support in its
  capabilities

This capability negotiation ensures clients and servers have a clear understanding of
supported functionality while maintaining protocol extensibility.


# Authorization
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

## Introduction

### Purpose and Scope

The Model Context Protocol provides authorization capabilities at the transport level,
enabling MCP clients to make requests to restricted MCP servers on behalf of resource
owners. This specification defines the authorization flow for HTTP-based transports.

### Protocol Requirements

Authorization is **OPTIONAL** for MCP implementations. When supported:

* Implementations using an HTTP-based transport **SHOULD** conform to this specification.
* Implementations using an STDIO transport **SHOULD NOT** follow this specification, and
  instead retrieve credentials from the environment.
* Implementations using alternative transports **MUST** follow established security best
  practices for their protocol.

### Standards Compliance

This authorization mechanism is based on established specifications listed below, but
implements a selected subset of their features to ensure security and interoperability
while maintaining simplicity:

* OAuth 2.1 IETF DRAFT ([draft-ietf-oauth-v2-1-12](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12))
* OAuth 2.0 Authorization Server Metadata
  ([RFC8414](https://datatracker.ietf.org/doc/html/rfc8414))
* OAuth 2.0 Dynamic Client Registration Protocol
  ([RFC7591](https://datatracker.ietf.org/doc/html/rfc7591))
* OAuth 2.0 Protected Resource Metadata ([RFC9728](https://datatracker.ietf.org/doc/html/rfc9728))

## Authorization Flow

### Roles

A protected *MCP server* acts as an [OAuth 2.1 resource server](https://www.ietf.org/archive/id/draft-ietf-oauth-v2-1-12.html#name-roles),
capable of accepting and responding to protected resource requests using access tokens.

An *MCP client* acts as an [OAuth 2.1 client](https://www.ietf.org/archive/id/draft-ietf-oauth-v2-1-12.html#name-roles),
making protected resource requests on behalf of a resource owner.

The *authorization server* is responsible for interacting with the user (if necessary) and issuing access tokens for use at the MCP server.
The implementation details of the authorization server are beyond the scope of this specification. It may be hosted with the
resource server or a separate entity. The [Authorization Server Discovery section](#authorization-server-discovery)
specifies how an MCP server indicates the location of its corresponding authorization server to a client.

### Overview

1. Authorization servers **MUST** implement OAuth 2.1 with appropriate security
   measures for both confidential and public clients.

2. Authorization servers and MCP clients **SHOULD** support the OAuth 2.0 Dynamic Client Registration
   Protocol ([RFC7591](https://datatracker.ietf.org/doc/html/rfc7591)).

3. MCP servers **MUST** implement OAuth 2.0 Protected Resource Metadata ([RFC9728](https://datatracker.ietf.org/doc/html/rfc9728)).
   MCP clients **MUST** use OAuth 2.0 Protected Resource Metadata for authorization server discovery.

4. Authorization servers **MUST** provide OAuth 2.0 Authorization
   Server Metadata ([RFC8414](https://datatracker.ietf.org/doc/html/rfc8414)).
   MCP clients **MUST** use the OAuth 2.0 Authorization Server Metadata.

### Authorization Server Discovery

This section describes the mechanisms by which MCP servers advertise their associated
authorization servers to MCP clients, as well as the discovery process through which MCP
clients can determine authorization server endpoints and supported capabilities.

#### Authorization Server Location

MCP servers **MUST** implement the OAuth 2.0 Protected Resource Metadata ([RFC9728](https://datatracker.ietf.org/doc/html/rfc9728))
specification to indicate the locations of authorization servers. The Protected Resource Metadata document returned by the MCP server **MUST** include
the `authorization_servers` field containing at least one authorization server.

The specific use of `authorization_servers` is beyond the scope of this specification; implementers should consult
OAuth 2.0 Protected Resource Metadata ([RFC9728](https://datatracker.ietf.org/doc/html/rfc9728)) for
guidance on implementation details.

Implementors should note that Protected Resource Metadata documents can define multiple authorization servers. The responsibility for selecting which authorization server to use lies with the MCP client, following the guidelines specified in
[RFC9728 Section 7.6 "Authorization Servers"](https://datatracker.ietf.org/doc/html/rfc9728#name-authorization-servers).

MCP servers **MUST** use the HTTP header `WWW-Authenticate` when returning a *401 Unauthorized* to indicate the location of the resource server metadata URL
as described in [RFC9728 Section 5.1 "WWW-Authenticate Response"](https://datatracker.ietf.org/doc/html/rfc9728#name-www-authenticate-response).

MCP clients **MUST** be able to parse `WWW-Authenticate` headers and respond appropriately to `HTTP 401 Unauthorized` responses from the MCP server.

#### Server Metadata Discovery

MCP clients **MUST** follow the OAuth 2.0 Authorization Server Metadata [RFC8414](https://datatracker.ietf.org/doc/html/rfc8414)
specification to obtain the information required to interact with the authorization server.

#### Sequence Diagram

The following diagram outlines an example flow:

```mermaid
sequenceDiagram
    participant C as Client
    participant M as MCP Server (Resource Server)
    participant A as Authorization Server

    C->>M: MCP request without token
    M-->>C: HTTP 401 Unauthorized with WWW-Authenticate header
    Note over C: Extract resource_metadata<br />from WWW-Authenticate

    C->>M: GET /.well-known/oauth-protected-resource
    M-->>C: Resource metadata with authorization server URL
    Note over C: Validate RS metadata,<br />build AS metadata URL

    C->>A: GET /.well-known/oauth-authorization-server
    A-->>C: Authorization server metadata

    Note over C,A: OAuth 2.1 authorization flow happens here

    C->>A: Token request
    A-->>C: Access token

    C->>M: MCP request with access token
    M-->>C: MCP response
    Note over C,M: MCP communication continues with valid token
```

### Dynamic Client Registration

MCP clients and authorization servers **SHOULD** support the
OAuth 2.0 Dynamic Client Registration Protocol [RFC7591](https://datatracker.ietf.org/doc/html/rfc7591)
to allow MCP clients to obtain OAuth client IDs without user interaction. This provides a
standardized way for clients to automatically register with new authorization servers, which is crucial
for MCP because:

* Clients may not know all possible MCP servers and their authorization servers in advance.
* Manual registration would create friction for users.
* It enables seamless connection to new MCP servers and their authorization servers.
* Authorization servers can implement their own registration policies.

Any authorization servers that *do not* support Dynamic Client Registration need to provide
alternative ways to obtain a client ID (and, if applicable, client credentials). For one of
these authorization servers, MCP clients will have to either:

1. Hardcode a client ID (and, if applicable, client credentials) specifically for the MCP client to use when
   interacting with that authorization server, or
2. Present a UI to users that allows them to enter these details, after registering an
   OAuth client themselves (e.g., through a configuration interface hosted by the
   server).

### Authorization Flow Steps

The complete Authorization flow proceeds as follows:

```mermaid
sequenceDiagram
    participant B as User-Agent (Browser)
    participant C as Client
    participant M as MCP Server (Resource Server)
    participant A as Authorization Server

    C->>M: MCP request without token
    M->>C: HTTP 401 Unauthorized with WWW-Authenticate header
    Note over C: Extract resource_metadata URL from WWW-Authenticate

    C->>M: Request Protected Resource Metadata
    M->>C: Return metadata

    Note over C: Parse metadata and extract authorization server(s)<br/>Client determines AS to use

    C->>A: GET /.well-known/oauth-authorization-server
    A->>C: Authorization server metadata response

    alt Dynamic client registration
        C->>A: POST /register
        A->>C: Client Credentials
    end

    Note over C: Generate PKCE parameters<br/>Include resource parameter
    C->>B: Open browser with authorization URL + code_challenge + resource
    B->>A: Authorization request with resource parameter
    Note over A: User authorizes
    A->>B: Redirect to callback with authorization code
    B->>C: Authorization code callback
    C->>A: Token request + code_verifier + resource
    A->>C: Access token (+ refresh token)
    C->>M: MCP request with access token
    M-->>C: MCP response
    Note over C,M: MCP communication continues with valid token
```

#### Resource Parameter Implementation

MCP clients **MUST** implement Resource Indicators for OAuth 2.0 as defined in [RFC 8707](https://www.rfc-editor.org/rfc/rfc8707.html)
to explicitly specify the target resource for which the token is being requested. The `resource` parameter:

1. **MUST** be included in both authorization requests and token requests.
2. **MUST** identify the MCP server that the client intends to use the token with.
3. **MUST** use the canonical URI of the MCP server as defined in [RFC 8707 Section 2](https://www.rfc-editor.org/rfc/rfc8707.html#name-access-token-request).

##### Canonical Server URI

For the purposes of this specification, the canonical URI of an MCP server is defined as the resource identifier as specified in
[RFC 8707 Section 2](https://www.rfc-editor.org/rfc/rfc8707.html#section-2) and aligns with the `resource` parameter in
[RFC 9728](https://datatracker.ietf.org/doc/html/rfc9728).

MCP clients **SHOULD** provide the most specific URI that they can for the MCP server they intend to access, following the guidance in [RFC 8707](https://www.rfc-editor.org/rfc/rfc8707). While the canonical form uses lowercase scheme and host components, implementations **SHOULD** accept uppercase scheme and host components for robustness and interoperability.

Examples of valid canonical URIs:

* `https://mcp.example.com/mcp`
* `https://mcp.example.com`
* `https://mcp.example.com:8443`
* `https://mcp.example.com/server/mcp` (when path component is necessary to identify individual MCP server)

Examples of invalid canonical URIs:

* `mcp.example.com` (missing scheme)
* `https://mcp.example.com#fragment` (contains fragment)

> **Note:** While both `https://mcp.example.com/` (with trailing slash) and `https://mcp.example.com` (without trailing slash) are technically valid absolute URIs according to [RFC 3986](https://www.rfc-editor.org/rfc/rfc3986), implementations **SHOULD** consistently use the form without the trailing slash for better interoperability unless the trailing slash is semantically significant for the specific resource.

For example, if accessing an MCP server at `https://mcp.example.com`, the authorization request would include:

```
&resource=https%3A%2F%2Fmcp.example.com
```

MCP clients **MUST** send this parameter regardless of whether authorization servers support it.

### Access Token Usage

#### Token Requirements

Access token handling when making requests to MCP servers **MUST** conform to the requirements defined in
[OAuth 2.1 Section 5 "Resource Requests"](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-5).
Specifically:

1. MCP client **MUST** use the Authorization request header field defined in
   [OAuth 2.1 Section 5.1.1](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-5.1.1):

```
Authorization: Bearer <access-token>
```

Note that authorization **MUST** be included in every HTTP request from client to server,
even if they are part of the same logical session.

2. Access tokens **MUST NOT** be included in the URI query string

Example request:

```http
GET /mcp HTTP/1.1
Host: mcp.example.com
Authorization: Bearer eyJhbGciOiJIUzI1NiIs...
```

#### Token Handling

MCP servers, acting in their role as an OAuth 2.1 resource server, **MUST** validate access tokens as described in
[OAuth 2.1 Section 5.2](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-5.2).
MCP servers **MUST** validate that access tokens were issued specifically for them as the intended audience,
according to [RFC 8707 Section 2](https://www.rfc-editor.org/rfc/rfc8707.html#section-2).
If validation fails, servers **MUST** respond according to
[OAuth 2.1 Section 5.3](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-5.3)
error handling requirements. Invalid or expired tokens **MUST** receive a HTTP 401
response.

MCP clients **MUST NOT** send tokens to the MCP server other than ones issued by the MCP server's authorization server.

Authorization servers **MUST** only accept tokens that are valid for use with their
own resources.

MCP servers **MUST NOT** accept or transit any other tokens.

### Error Handling

Servers **MUST** return appropriate HTTP status codes for authorization errors:

| Status Code | Description  | Usage                                      |
| ----------- | ------------ | ------------------------------------------ |
| 401         | Unauthorized | Authorization required or token invalid    |
| 403         | Forbidden    | Invalid scopes or insufficient permissions |
| 400         | Bad Request  | Malformed authorization request            |

## Security Considerations

Implementations **MUST** follow OAuth 2.1 security best practices as laid out in [OAuth 2.1 Section 7. "Security Considerations"](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#name-security-considerations).

### Token Audience Binding and Validation

[RFC 8707](https://www.rfc-editor.org/rfc/rfc8707.html) Resource Indicators provide critical security benefits by binding tokens to their intended
audiences **when the Authorization Server supports the capability**. To enable current and future adoption:

* MCP clients **MUST** include the `resource` parameter in authorization and token requests as specified in the [Resource Parameter Implementation](#resource-parameter-implementation) section
* MCP servers **MUST** validate that tokens presented to them were specifically issued for their use

The [Security Best Practices document](/specification/draft/basic/security_best_practices#token-passthrough)
outlines why token audience validation is crucial and why token passthrough is explicitly forbidden.

### Token Theft

Attackers who obtain tokens stored by the client, or tokens cached or logged on the server can access protected resources with
requests that appear legitimate to resource servers.

Clients and servers **MUST** implement secure token storage and follow OAuth best practices,
as outlined in [OAuth 2.1, Section 7.1](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-7.1).

Authorization servers **SHOULD** issue short-lived access tokens to reduce the impact of leaked tokens.
For public clients, authorization servers **MUST** rotate refresh tokens as described in [OAuth 2.1 Section 4.3.1 "Refresh Token Grant"](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-4.3.1).

### Communication Security

Implementations **MUST** follow [OAuth 2.1 Section 1.5 "Communication Security"](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-1.5).

Specifically:

1. All authorization server endpoints **MUST** be served over HTTPS.
2. All redirect URIs **MUST** be either `localhost` or use HTTPS.

### Authorization Code Protection

An attacker who has gained access to an authorization code contained in an authorization response can try to redeem the authorization code for an access token or otherwise make use of the authorization code.
(Further described in [OAuth 2.1 Section 7.5](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-7.5))

To mitigate this, MCP clients **MUST** implement PKCE according to [OAuth 2.1 Section 7.5.2](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-7.5.2).
PKCE helps prevent authorization code interception and injection attacks by requiring clients to create a secret verifier-challenge pair, ensuring that only the original requestor can exchange an authorization code for tokens.

### Open Redirection

An attacker may craft malicious redirect URIs to direct users to phishing sites.

MCP clients **MUST** have redirect URIs registered with the authorization server.

Authorization servers **MUST** validate exact redirect URIs against pre-registered values to prevent redirection attacks.

MCP clients **SHOULD** use and verify state parameters in the authorization code flow
and discard any results that do not include or have a mismatch with the original state.

Authorization servers **MUST** take precautions to prevent redirecting user agents to untrusted URI's, following suggestions laid out in [OAuth 2.1 Section 7.12.2](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-12#section-7.12.2)

Authorization servers **SHOULD** only automatically redirect the user agent if it trusts the redirection URI. If the URI is not trusted, the authorization server MAY inform the user and rely on the user to make the correct decision.

### Confused Deputy Problem

Attackers can exploit MCP servers acting as intermediaries to third-party APIs, leading to [confused deputy vulnerabilities](/specification/2025-06-18/basic/security_best_practices#confused-deputy-problem).
By using stolen authorization codes, they can obtain access tokens without user consent.

MCP proxy servers using static client IDs **MUST** obtain user consent for each dynamically
registered client before forwarding to third-party authorization servers (which may require additional consent).

### Access Token Privilege Restriction

An attacker can gain unauthorized access or otherwise compromise a MCP server if the server accepts tokens issued for other resources.

This vulnerability has two critical dimensions:

1. **Audience validation failures.** When an MCP server doesn't verify that tokens were specifically intended for it (for example, via the audience claim, as mentioned in [RFC9068](https://www.rfc-editor.org/rfc/rfc9068.html)), it may accept tokens originally issued for other services. This breaks a fundamental OAuth security boundary, allowing attackers to reuse legitimate tokens across different services than intended.
2. **Token passthrough.** If the MCP server not only accepts tokens with incorrect audiences but also forwards these unmodified tokens to downstream services, it can potentially cause the ["confused deputy" problem](#confused-deputy-problem), where the downstream API may incorrectly trust the token as if it came from the MCP server or assume the token was validated by the upstream API. See the [Token Passthrough section](/specification/2025-06-18/basic/security_best_practices#token-passthrough) of the Security Best Practices guide for additional details.

MCP servers **MUST** validate access tokens before processing the request, ensuring the access token is issued specifically for the MCP server, and take all necessary steps to ensure no data is returned to unauthorized parties.

A MCP server **MUST** follow the guidelines in [OAuth 2.1 - Section 5.2](https://www.ietf.org/archive/id/draft-ietf-oauth-v2-1-12.html#section-5.2) to validate inbound tokens.

MCP servers **MUST** only accept tokens specifically intended for themselves and **MUST** reject tokens that do not include them in the audience claim or otherwise verify that they are the intended recipient of the token. See the [Security Best Practices Token Passthrough section](/specification/2025-06-18/basic/security_best_practices#token-passthrough) for details.

If the MCP server makes requests to upstream APIs, it may act as an OAuth client to them. The access token used at the upstream API is a seperate token, issued by the upstream authorization server. The MCP server **MUST NOT** pass through the token it received from the MCP client.

MCP clients **MUST** implement and use the `resource` parameter as defined in [RFC 8707 - Resource Indicators for OAuth 2.0](https://www.rfc-editor.org/rfc/rfc8707.html)
to explicitly specify the target resource for which the token is being requested. This requirement aligns with the recommendation in
[RFC 9728 Section 7.4](https://datatracker.ietf.org/doc/html/rfc9728#section-7.4). This ensures that access tokens are bound to their intended resources and
cannot be misused across different services.


# Overview
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/index



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol consists of several key components that work together:

* **Base Protocol**: Core JSON-RPC message types
* **Lifecycle Management**: Connection initialization, capability negotiation, and
  session control
* **Authorization**: Authentication and authorization framework for HTTP-based transports
* **Server Features**: Resources, prompts, and tools exposed by servers
* **Client Features**: Sampling and root directory lists provided by clients
* **Utilities**: Cross-cutting concerns like logging and argument completion

All implementations **MUST** support the base protocol and lifecycle management
components. Other components **MAY** be implemented based on the specific needs of the
application.

These protocol layers establish clear separation of concerns while enabling rich
interactions between clients and servers. The modular design allows implementations to
support exactly the features they need.

## Messages

All messages between MCP clients and servers **MUST** follow the
[JSON-RPC 2.0](https://www.jsonrpc.org/specification) specification. The protocol defines
these types of messages:

### Requests

Requests are sent from the client to the server or vice versa, to initiate an operation.

```typescript
{
  jsonrpc: "2.0";
  id: string | number;
  method: string;
  params?: {
    [key: string]: unknown;
  };
}
```

* Requests **MUST** include a string or integer ID.
* Unlike base JSON-RPC, the ID **MUST NOT** be `null`.
* The request ID **MUST NOT** have been previously used by the requestor within the same
  session.

### Responses

Responses are sent in reply to requests, containing the result or error of the operation.

```typescript
{
  jsonrpc: "2.0";
  id: string | number;
  result?: {
    [key: string]: unknown;
  }
  error?: {
    code: number;
    message: string;
    data?: unknown;
  }
}
```

* Responses **MUST** include the same ID as the request they correspond to.
* **Responses** are further sub-categorized as either **successful results** or
  **errors**. Either a `result` or an `error` **MUST** be set. A response **MUST NOT**
  set both.
* Results **MAY** follow any JSON object structure, while errors **MUST** include an
  error code and message at minimum.
* Error codes **MUST** be integers.

### Notifications

Notifications are sent from the client to the server or vice versa, as a one-way message.
The receiver **MUST NOT** send a response.

```typescript
{
  jsonrpc: "2.0";
  method: string;
  params?: {
    [key: string]: unknown;
  };
}
```

* Notifications **MUST NOT** include an ID.

## Auth

MCP provides an [Authorization](/specification/2025-06-18/basic/authorization) framework for use with HTTP.
Implementations using an HTTP-based transport **SHOULD** conform to this specification,
whereas implementations using STDIO transport **SHOULD NOT** follow this specification,
and instead retrieve credentials from the environment.

Additionally, clients and servers **MAY** negotiate their own custom authentication and
authorization strategies.

For further discussions and contributions to the evolution of MCP’s auth mechanisms, join
us in
[GitHub Discussions](https://github.com/modelcontextprotocol/specification/discussions)
to help shape the future of the protocol!

## Schema

The full specification of the protocol is defined as a
[TypeScript schema](https://github.com/modelcontextprotocol/specification/blob/main/schema/2025-06-18/schema.ts).
This is the source of truth for all protocol messages and structures.

There is also a
[JSON Schema](https://github.com/modelcontextprotocol/specification/blob/main/schema/2025-06-18/schema.json),
which is automatically generated from the TypeScript source of truth, for use with
various automated tooling.

### General fields

#### `_meta`

The `_meta` property/parameter is reserved by MCP to allow clients and servers
to attach additional metadata to their interactions.

Certain key names are reserved by MCP for protocol-level metadata, as specified below;
implementations MUST NOT make assumptions about values at these keys.

Additionally, definitions in the [schema](https://github.com/modelcontextprotocol/specification/blob/main/schema/2025-06-18/schema.ts)
may reserve particular names for purpose-specific metadata, as declared in those definitions.

**Key name format:** valid `_meta` key names have two segments: an optional **prefix**, and a **name**.

**Prefix:**

* If specified, MUST be a series of labels separated by dots (`.`), followed by a slash (`/`).
  * Labels MUST start with a letter and end with a letter or digit; interior characters can be letters, digits, or hyphens (`-`).
* Any prefix beginning with zero or more valid labels, followed by `modelcontextprotocol` or `mcp`, followed by any valid label,
  is **reserved** for MCP use.
  * For example: `modelcontextprotocol.io/`, `mcp.dev/`, `api.modelcontextprotocol.org/`, and `tools.mcp.com/` are all reserved.

**Name:**

* Unless empty, MUST begin and end with an alphanumeric character (`[a-z0-9A-Z]`).
* MAY contain hyphens (`-`), underscores (`_`), dots (`.`), and alphanumerics in between.


# Lifecycle
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/lifecycle



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) defines a rigorous lifecycle for client-server
connections that ensures proper capability negotiation and state management.

1. **Initialization**: Capability negotiation and protocol version agreement
2. **Operation**: Normal protocol communication
3. **Shutdown**: Graceful termination of the connection

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Note over Client,Server: Initialization Phase
    activate Client
    Client->>+Server: initialize request
    Server-->>Client: initialize response
    Client--)Server: initialized notification

    Note over Client,Server: Operation Phase
    rect rgb(200, 220, 250)
        note over Client,Server: Normal protocol operations
    end

    Note over Client,Server: Shutdown
    Client--)-Server: Disconnect
    deactivate Server
    Note over Client,Server: Connection closed
```

## Lifecycle Phases

### Initialization

The initialization phase **MUST** be the first interaction between client and server.
During this phase, the client and server:

* Establish protocol version compatibility
* Exchange and negotiate capabilities
* Share implementation details

The client **MUST** initiate this phase by sending an `initialize` request containing:

* Protocol version supported
* Client capabilities
* Client implementation information

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "roots": {
        "listChanged": true
      },
      "sampling": {},
      "elicitation": {}
    },
    "clientInfo": {
      "name": "ExampleClient",
      "title": "Example Client Display Name",
      "version": "1.0.0"
    }
  }
}
```

The server **MUST** respond with its own capabilities and information:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "protocolVersion": "2024-11-05",
    "capabilities": {
      "logging": {},
      "prompts": {
        "listChanged": true
      },
      "resources": {
        "subscribe": true,
        "listChanged": true
      },
      "tools": {
        "listChanged": true
      }
    },
    "serverInfo": {
      "name": "ExampleServer",
      "title": "Example Server Display Name",
      "version": "1.0.0"
    },
    "instructions": "Optional instructions for the client"
  }
}
```

After successful initialization, the client **MUST** send an `initialized` notification
to indicate it is ready to begin normal operations:

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/initialized"
}
```

* The client **SHOULD NOT** send requests other than
  [pings](/specification/2025-06-18/basic/utilities/ping) before the server has responded to the
  `initialize` request.
* The server **SHOULD NOT** send requests other than
  [pings](/specification/2025-06-18/basic/utilities/ping) and
  [logging](/specification/2025-06-18/server/utilities/logging) before receiving the `initialized`
  notification.

#### Version Negotiation

In the `initialize` request, the client **MUST** send a protocol version it supports.
This **SHOULD** be the *latest* version supported by the client.

If the server supports the requested protocol version, it **MUST** respond with the same
version. Otherwise, the server **MUST** respond with another protocol version it
supports. This **SHOULD** be the *latest* version supported by the server.

If the client does not support the version in the server's response, it **SHOULD**
disconnect.

<Note>
  If using HTTP, the client **MUST** include the `MCP-Protocol-Version: <protocol-version>` HTTP header on all subsequent requests to the MCP
  server.
  For details, see [the Protocol Version Header section in Transports](/specification/2025-06-18/basic/transports#protocol-version-header).
</Note>

#### Capability Negotiation

Client and server capabilities establish which optional protocol features will be
available during the session.

Key capabilities include:

| Category | Capability     | Description                                                                               |
| -------- | -------------- | ----------------------------------------------------------------------------------------- |
| Client   | `roots`        | Ability to provide filesystem [roots](/specification/2025-06-18/client/roots)             |
| Client   | `sampling`     | Support for LLM [sampling](/specification/2025-06-18/client/sampling) requests            |
| Client   | `elicitation`  | Support for server [elicitation](/specification/2025-06-18/client/elicitation) requests   |
| Client   | `experimental` | Describes support for non-standard experimental features                                  |
| Server   | `prompts`      | Offers [prompt templates](/specification/2025-06-18/server/prompts)                       |
| Server   | `resources`    | Provides readable [resources](/specification/2025-06-18/server/resources)                 |
| Server   | `tools`        | Exposes callable [tools](/specification/2025-06-18/server/tools)                          |
| Server   | `logging`      | Emits structured [log messages](/specification/2025-06-18/server/utilities/logging)       |
| Server   | `completions`  | Supports argument [autocompletion](/specification/2025-06-18/server/utilities/completion) |
| Server   | `experimental` | Describes support for non-standard experimental features                                  |

Capability objects can describe sub-capabilities like:

* `listChanged`: Support for list change notifications (for prompts, resources, and
  tools)
* `subscribe`: Support for subscribing to individual items' changes (resources only)

### Operation

During the operation phase, the client and server exchange messages according to the
negotiated capabilities.

Both parties **MUST**:

* Respect the negotiated protocol version
* Only use capabilities that were successfully negotiated

### Shutdown

During the shutdown phase, one side (usually the client) cleanly terminates the protocol
connection. No specific shutdown messages are defined—instead, the underlying transport
mechanism should be used to signal connection termination:

#### stdio

For the stdio [transport](/specification/2025-06-18/basic/transports), the client **SHOULD** initiate
shutdown by:

1. First, closing the input stream to the child process (the server)
2. Waiting for the server to exit, or sending `SIGTERM` if the server does not exit
   within a reasonable time
3. Sending `SIGKILL` if the server does not exit within a reasonable time after `SIGTERM`

The server **MAY** initiate shutdown by closing its output stream to the client and
exiting.

#### HTTP

For HTTP [transports](/specification/2025-06-18/basic/transports), shutdown is indicated by closing the
associated HTTP connection(s).

## Timeouts

Implementations **SHOULD** establish timeouts for all sent requests, to prevent hung
connections and resource exhaustion. When the request has not received a success or error
response within the timeout period, the sender **SHOULD** issue a [cancellation
notification](/specification/2025-06-18/basic/utilities/cancellation) for that request and stop waiting for
a response.

SDKs and other middleware **SHOULD** allow these timeouts to be configured on a
per-request basis.

Implementations **MAY** choose to reset the timeout clock when receiving a [progress
notification](/specification/2025-06-18/basic/utilities/progress) corresponding to the request, as this
implies that work is actually happening. However, implementations **SHOULD** always
enforce a maximum timeout, regardless of progress notifications, to limit the impact of a
misbehaving client or server.

## Error Handling

Implementations **SHOULD** be prepared to handle these error cases:

* Protocol version mismatch
* Failure to negotiate required capabilities
* Request [timeouts](#timeouts)

Example initialization error:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "error": {
    "code": -32602,
    "message": "Unsupported protocol version",
    "data": {
      "supported": ["2024-11-05"],
      "requested": "1.0.0"
    }
  }
}
```


# Security Best Practices
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/security_best_practices



<div id="enable-section-numbers" />

## Introduction

### Purpose and Scope

This document provides security considerations for the Model Context Protocol (MCP), complementing the MCP Authorization specification. This document identifies security risks, attack vectors, and best practices specific to MCP implementations.

The primary audience for this document includes developers implementing MCP authorization flows, MCP server operators, and security professionals evaluating MCP-based systems. This document should be read alongside the MCP Authorization specification and [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700).

## Attacks and Mitigations

This section gives a detailed description of attacks on MCP implementations, along with potential countermeasures.

### Confused Deputy Problem

Attackers can exploit MCP servers proxying other resource servers, creating "[confused deputy](https://en.wikipedia.org/wiki/Confused_deputy_problem)" vulnerabilities.

#### Terminology

**MCP Proxy Server**
: An MCP server that connects MCP clients to third-party APIs, offering MCP features while delegating operations and acting as a single OAuth client to the third-party API server.

**Third-Party Authorization Server**
: Authorization server that protects the third-party API. It may lack dynamic client registration support, requiring MCP proxy to use a static client ID for all requests.

**Third-Party API**
: The protected resource server that provides the actual API functionality. Access to this
API requires tokens issued by the third-party authorization server.

**Static Client ID**
: A fixed OAuth 2.0 client identifier used by the MCP proxy server when communicating with
the third-party authorization server. This Client ID refers to the MCP server acting as a client
to the Third-Party API. It is the same value for all MCP server to Third-Party API interactions regardless of
which MCP client initiated the request.

#### Architecture and Attack Flows

##### Normal OAuth proxy usage (preserves user consent)

```mermaid
sequenceDiagram
    participant UA as User-Agent (Browser)
    participant MC as MCP Client
    participant M as MCP Proxy Server
    participant TAS as Third-Party Authorization Server

    Note over UA,M: Initial Auth flow completed

    Note over UA,TAS: Step 1: Legitimate user consent for Third Party Server

    M->>UA: Redirect to third party authorization server
    UA->>TAS: Authorization request (client_id: mcp-proxy)
    TAS->>UA: Authorization consent screen
    Note over UA: Review consent screen
    UA->>TAS: Approve
    TAS->>UA: Set consent cookie for client ID: mcp-proxy
    TAS->>UA: 3P Authorization code + redirect to mcp-proxy-server.com
    UA->>M: 3P Authorization code
    Note over M,TAS: Exchange 3P code for 3P token
    Note over M: Generate MCP authorization code
    M->>UA: Redirect to MCP Client with MCP authorization code

    Note over M,UA: Exchange code for token, etc.
```

##### Malicious OAuth proxy usage (skips user consent)

```mermaid
sequenceDiagram
    participant UA as User-Agent (Browser)
    participant M as MCP Proxy Server
    participant TAS as Third-Party Authorization Server
    participant A as Attacker


    Note over UA,A: Step 2: Attack (leveraging existing cookie, skipping consent)
    A->>M: Dynamically register malicious client, redirect_uri: attacker.com
    A->>UA: Sends malicious link
    UA->>TAS: Authorization request (client_id: mcp-proxy) + consent cookie
    rect rgba(255, 17, 0, 0.67)
    TAS->>TAS: Cookie present, consent skipped
    end

   TAS->>UA: 3P Authorization code + redirect to mcp-proxy-server.com
   UA->>M: 3P Authorization code
   Note over M,TAS: Exchange 3P code for 3P token
   Note over M: Generate MCP authorization code
   M->>UA: Redirect to attacker.com with MCP Authorization code
   UA->>A: MCP Authorization code delivered to attacker.com
   Note over M,A: Attacker exchanges MCP code for MCP token
   A->>M: Attacker impersonates user to MCP server
```

#### Attack Description

When an MCP proxy server uses a static client ID to authenticate with a third-party
authorization server that does not support dynamic client registration, the following
attack becomes possible:

1. A user authenticates normally through the MCP proxy server to access the third-party API
2. During this flow, the third-party authorization server sets a cookie on the user agent
   indicating consent for the static client ID
3. An attacker later sends the user a malicious link containing a crafted authorization request which contains a malicious redirect URI along with a new dynamically registered client ID
4. When the user clicks the link, their browser still has the consent cookie from the previous legitimate request
5. The third-party authorization server detects the cookie and skips the consent screen
6. The MCP authorization code is redirected to the attacker's server (specified in the crafted redirect\_uri during dynamic client registration)
7. The attacker exchanges the stolen authorization code for access tokens for the MCP server without the user's explicit approval
8. Attacker now has access to the third-party API as the compromised user

#### Mitigation

MCP proxy servers using static client IDs **MUST** obtain user consent for each dynamically
registered client before forwarding to third-party authorization servers (which may require additional consent).

### Token Passthrough

"Token passthrough" is an anti-pattern where an MCP server accepts tokens from an MCP client without validating that the tokens were properly issued *to the MCP server* and "passing them through" to the downstream API.

#### Risks

Token passthrough is explicitly forbidden in the [authorization specification](/specification/2025-06-18/basic/authorization) as it introduces a number of security risks, that include:

* **Security Control Circumvention**
  * The MCP Server or downstream APIs might implement important security controls like rate limiting, request validation, or traffic monitoring, that depend on the token audience or other credential constraints. If clients can obtain and use tokens directly with the downstream APIs without the MCP server validating them properly or ensuring that the tokens are issued for the right service, they bypass these controls.
* **Accountability and Audit Trail Issues**
  * The MCP Server will be unable to identify or distinguish between MCP Clients when clients are calling with an upstream-issued access token which may be opaque to the MCP Server.
  * The downstream Resource Server’s logs may show requests that appear to come from a different source with a different identity, rather than the MCP server that is actually forwarding the tokens.
  * Both factors make incident investigation, controls, and auditing more difficult.
  * If the MCP Server passes tokens without validating their claims (e.g., roles, privileges, or audience) or other metadata, a malicious actor in possession of a stolen token can use the server as a proxy for data exfiltration.
* **Trust Boundary Issues**
  * The downstream Resource Server grants trust to specific entities. This trust might include assumptions about origin or client behavior patterns. Breaking this trust boundary could lead to unexpected issues.
  * If the token is accepted by multiple services without proper validation, an attacker compromising one service can use the token to access other connected services.
* **Future Compatibility Risk**
  * Even if an MCP Server starts as a "pure proxy" today, it might need to add security controls later. Starting with proper token audience separation makes it easier to evolve the security model.

#### Mitigation

MCP servers **MUST NOT** accept any tokens that were not explicitly issued for the MCP server.

### Session Hijacking

Session hijacking is an attack vector where a client is provided a session ID by the server, and an unauthorized party is able to obtain and use that same session ID to impersonate the original client and perform unauthorized actions on their behalf.

#### Session Hijack Prompt Injection

```mermaid
sequenceDiagram
    participant Client
    participant ServerA
    participant Queue
    participant ServerB
    participant Attacker

    Client->>ServerA: Initialize (connect to streamable HTTP server)
    ServerA-->>Client: Respond with session ID

    Attacker->>ServerB: Access/guess session ID
    Note right of Attacker: Attacker knows/guesses session ID

    Attacker->>ServerB: Trigger event (malicious payload, using session ID)
    ServerB->>Queue: Enqueue event (keyed by session ID)

    ServerA->>Queue: Poll for events (using session ID)
    Queue-->>ServerA: Event data (malicious payload)

    ServerA-->>Client: Async response (malicious payload)
    Client->>Client: Acts based on malicious payload
```

#### Session Hijack Impersonation

```mermaid
sequenceDiagram
    participant Client
    participant Server
    participant Attacker

    Client->>Server: Initialize (login/authenticate)
    Server-->>Client: Respond with session ID (persistent session created)

    Attacker->>Server: Access/guess session ID
    Note right of Attacker: Attacker knows/guesses session ID

    Attacker->>Server: Make API call (using session ID, no re-auth)
    Server-->>Attacker: Respond as if Attacker is Client (session hijack)
```

#### Attack Description

When you have multiple stateful HTTP servers that handle MCP requests, the following attack vectors are possible:

**Session Hijack Prompt Injection**

1. The client connects to **Server A** and receives a session ID.

2. The attacker obtains an existing session ID and sends a malicious event to **Server B** with said session ID.

   * When a server supports [redelivery/resumable streams](/specification/2025-06-18/basic/transports#resumability-and-redelivery), deliberately terminating the request before receiving the response could lead to it being resumed by the original client via the GET request for server sent events.
   * If a particular server initiates server sent events as a consequence of a tool call such as a `notifications/tools/list_changed`, where it is possible to affect the tools that are offered by the server, a client could end up with tools that they were not aware were enabled.

3. **Server B** enqueues the event (associated with session ID) into a shared queue.

4. **Server A** polls the queue for events using the session ID and retrieves the malicious payload.

5. **Server A** sends the malicious payload to the client as an asynchronous or resumed response.

6. The client receives and acts on the malicious payload, leading to potential compromise.

**Session Hijack Impersonation**

1. The MCP client authenticates with the MCP server, creating a persistent session ID.
2. The attacker obtains the session ID.
3. The attacker makes calls to the MCP server using the session ID.
4. MCP server does not check for additional authorization and treats the attacker as a legitimate user, allowing unauthorized access or actions.

#### Mitigation

To prevent session hijacking and event injection attacks, the following mitigations should be implemented:

MCP servers that implement authorization **MUST** verify all inbound requests.
MCP Servers **MUST NOT** use sessions for authentication.

MCP servers **MUST** use secure, non-deterministic session IDs.
Generated session IDs (e.g., UUIDs) **SHOULD** use secure random number generators. Avoid predictable or sequential session identifiers that could be guessed by an attacker. Rotating or expiring session IDs can also reduce the risk.

MCP servers **SHOULD** bind session IDs to user-specific information.
When storing or transmitting session-related data (e.g., in a queue), combine the session ID with information unique to the authorized user, such as their internal user ID. Use a key format like `<user_id>:<session_id>`. This ensures that even if an attacker guesses a session ID, they cannot impersonate another user as the user ID is derived from the user token and not provided by the client.

MCP servers can optionally leverage additional unique identifiers.


# Transports
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/transports



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

MCP uses JSON-RPC to encode messages. JSON-RPC messages **MUST** be UTF-8 encoded.

The protocol currently defines two standard transport mechanisms for client-server
communication:

1. [stdio](#stdio), communication over standard in and standard out
2. [Streamable HTTP](#streamable-http)

Clients **SHOULD** support stdio whenever possible.

It is also possible for clients and servers to implement
[custom transports](#custom-transports) in a pluggable fashion.

## stdio

In the **stdio** transport:

* The client launches the MCP server as a subprocess.
* The server reads JSON-RPC messages from its standard input (`stdin`) and sends messages
  to its standard output (`stdout`).
* Messages are individual JSON-RPC requests, notifications, or responses.
* Messages are delimited by newlines, and **MUST NOT** contain embedded newlines.
* The server **MAY** write UTF-8 strings to its standard error (`stderr`) for logging
  purposes. Clients **MAY** capture, forward, or ignore this logging.
* The server **MUST NOT** write anything to its `stdout` that is not a valid MCP message.
* The client **MUST NOT** write anything to the server's `stdin` that is not a valid MCP
  message.

```mermaid
sequenceDiagram
    participant Client
    participant Server Process

    Client->>+Server Process: Launch subprocess
    loop Message Exchange
        Client->>Server Process: Write to stdin
        Server Process->>Client: Write to stdout
        Server Process--)Client: Optional logs on stderr
    end
    Client->>Server Process: Close stdin, terminate subprocess
    deactivate Server Process
```

## Streamable HTTP

<Info>
  This replaces the [HTTP+SSE
  transport](/specification/2024-11-05/basic/transports#http-with-sse) from
  protocol version 2024-11-05. See the [backwards compatibility](#backwards-compatibility)
  guide below.
</Info>

In the **Streamable HTTP** transport, the server operates as an independent process that
can handle multiple client connections. This transport uses HTTP POST and GET requests.
Server can optionally make use of
[Server-Sent Events](https://en.wikipedia.org/wiki/Server-sent_events) (SSE) to stream
multiple server messages. This permits basic MCP servers, as well as more feature-rich
servers supporting streaming and server-to-client notifications and requests.

The server **MUST** provide a single HTTP endpoint path (hereafter referred to as the
**MCP endpoint**) that supports both POST and GET methods. For example, this could be a
URL like `https://example.com/mcp`.

#### Security Warning

When implementing Streamable HTTP transport:

1. Servers **MUST** validate the `Origin` header on all incoming connections to prevent DNS rebinding attacks
2. When running locally, servers **SHOULD** bind only to localhost (127.0.0.1) rather than all network interfaces (0.0.0.0)
3. Servers **SHOULD** implement proper authentication for all connections

Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.

### Sending Messages to the Server

Every JSON-RPC message sent from the client **MUST** be a new HTTP POST request to the
MCP endpoint.

1. The client **MUST** use HTTP POST to send JSON-RPC messages to the MCP endpoint.
2. The client **MUST** include an `Accept` header, listing both `application/json` and
   `text/event-stream` as supported content types.
3. The body of the POST request **MUST** be a single JSON-RPC *request*, *notification*, or *response*.
4. If the input is a JSON-RPC *response* or *notification*:
   * If the server accepts the input, the server **MUST** return HTTP status code 202
     Accepted with no body.
   * If the server cannot accept the input, it **MUST** return an HTTP error status code
     (e.g., 400 Bad Request). The HTTP response body **MAY** comprise a JSON-RPC *error
     response* that has no `id`.
5. If the input is a JSON-RPC *request*, the server **MUST** either
   return `Content-Type: text/event-stream`, to initiate an SSE stream, or
   `Content-Type: application/json`, to return one JSON object. The client **MUST**
   support both these cases.
6. If the server initiates an SSE stream:
   * The SSE stream **SHOULD** eventually include JSON-RPC *response* for the
     JSON-RPC *request* sent in the POST body.
   * The server **MAY** send JSON-RPC *requests* and *notifications* before sending the
     JSON-RPC *response*. These messages **SHOULD** relate to the originating client
     *request*.
   * The server **SHOULD NOT** close the SSE stream before sending the JSON-RPC *response*
     for the received JSON-RPC *request*, unless the [session](#session-management)
     expires.
   * After the JSON-RPC *response* has been sent, the server **SHOULD** close the SSE
     stream.
   * Disconnection **MAY** occur at any time (e.g., due to network conditions).
     Therefore:
     * Disconnection **SHOULD NOT** be interpreted as the client cancelling its request.
     * To cancel, the client **SHOULD** explicitly send an MCP `CancelledNotification`.
     * To avoid message loss due to disconnection, the server **MAY** make the stream
       [resumable](#resumability-and-redelivery).

### Listening for Messages from the Server

1. The client **MAY** issue an HTTP GET to the MCP endpoint. This can be used to open an
   SSE stream, allowing the server to communicate to the client, without the client first
   sending data via HTTP POST.
2. The client **MUST** include an `Accept` header, listing `text/event-stream` as a
   supported content type.
3. The server **MUST** either return `Content-Type: text/event-stream` in response to
   this HTTP GET, or else return HTTP 405 Method Not Allowed, indicating that the server
   does not offer an SSE stream at this endpoint.
4. If the server initiates an SSE stream:
   * The server **MAY** send JSON-RPC *requests* and *notifications* on the stream.
   * These messages **SHOULD** be unrelated to any concurrently-running JSON-RPC
     *request* from the client.
   * The server **MUST NOT** send a JSON-RPC *response* on the stream **unless**
     [resuming](#resumability-and-redelivery) a stream associated with a previous client
     request.
   * The server **MAY** close the SSE stream at any time.
   * The client **MAY** close the SSE stream at any time.

### Multiple Connections

1. The client **MAY** remain connected to multiple SSE streams simultaneously.
2. The server **MUST** send each of its JSON-RPC messages on only one of the connected
   streams; that is, it **MUST NOT** broadcast the same message across multiple streams.
   * The risk of message loss **MAY** be mitigated by making the stream
     [resumable](#resumability-and-redelivery).

### Resumability and Redelivery

To support resuming broken connections, and redelivering messages that might otherwise be
lost:

1. Servers **MAY** attach an `id` field to their SSE events, as described in the
   [SSE standard](https://html.spec.whatwg.org/multipage/server-sent-events.html#event-stream-interpretation).
   * If present, the ID **MUST** be globally unique across all streams within that
     [session](#session-management)—or all streams with that specific client, if session
     management is not in use.
2. If the client wishes to resume after a broken connection, it **SHOULD** issue an HTTP
   GET to the MCP endpoint, and include the
   [`Last-Event-ID`](https://html.spec.whatwg.org/multipage/server-sent-events.html#the-last-event-id-header)
   header to indicate the last event ID it received.
   * The server **MAY** use this header to replay messages that would have been sent
     after the last event ID, *on the stream that was disconnected*, and to resume the
     stream from that point.
   * The server **MUST NOT** replay messages that would have been delivered on a
     different stream.

In other words, these event IDs should be assigned by servers on a *per-stream* basis, to
act as a cursor within that particular stream.

### Session Management

An MCP "session" consists of logically related interactions between a client and a
server, beginning with the [initialization phase](/specification/2025-06-18/basic/lifecycle). To support
servers which want to establish stateful sessions:

1. A server using the Streamable HTTP transport **MAY** assign a session ID at
   initialization time, by including it in an `Mcp-Session-Id` header on the HTTP
   response containing the `InitializeResult`.
   * The session ID **SHOULD** be globally unique and cryptographically secure (e.g., a
     securely generated UUID, a JWT, or a cryptographic hash).
   * The session ID **MUST** only contain visible ASCII characters (ranging from 0x21 to
     0x7E).
2. If an `Mcp-Session-Id` is returned by the server during initialization, clients using
   the Streamable HTTP transport **MUST** include it in the `Mcp-Session-Id` header on
   all of their subsequent HTTP requests.
   * Servers that require a session ID **SHOULD** respond to requests without an
     `Mcp-Session-Id` header (other than initialization) with HTTP 400 Bad Request.
3. The server **MAY** terminate the session at any time, after which it **MUST** respond
   to requests containing that session ID with HTTP 404 Not Found.
4. When a client receives HTTP 404 in response to a request containing an
   `Mcp-Session-Id`, it **MUST** start a new session by sending a new `InitializeRequest`
   without a session ID attached.
5. Clients that no longer need a particular session (e.g., because the user is leaving
   the client application) **SHOULD** send an HTTP DELETE to the MCP endpoint with the
   `Mcp-Session-Id` header, to explicitly terminate the session.
   * The server **MAY** respond to this request with HTTP 405 Method Not Allowed,
     indicating that the server does not allow clients to terminate sessions.

### Sequence Diagram

```mermaid
sequenceDiagram
    participant Client
    participant Server

    note over Client, Server: initialization

    Client->>+Server: POST InitializeRequest
    Server->>-Client: InitializeResponse<br>Mcp-Session-Id: 1868a90c...

    Client->>+Server: POST InitializedNotification<br>Mcp-Session-Id: 1868a90c...
    Server->>-Client: 202 Accepted

    note over Client, Server: client requests
    Client->>+Server: POST ... request ...<br>Mcp-Session-Id: 1868a90c...

    alt single HTTP response
      Server->>Client: ... response ...
    else server opens SSE stream
      loop while connection remains open
          Server-)Client: ... SSE messages from server ...
      end
      Server-)Client: SSE event: ... response ...
    end
    deactivate Server

    note over Client, Server: client notifications/responses
    Client->>+Server: POST ... notification/response ...<br>Mcp-Session-Id: 1868a90c...
    Server->>-Client: 202 Accepted

    note over Client, Server: server requests
    Client->>+Server: GET<br>Mcp-Session-Id: 1868a90c...
    loop while connection remains open
        Server-)Client: ... SSE messages from server ...
    end
    deactivate Server

```

### Protocol Version Header

If using HTTP, the client **MUST** include the `MCP-Protocol-Version: <protocol-version>` HTTP header on all subsequent requests to the MCP
server, allowing the MCP server to respond based on the MCP protocol version.

For example: `MCP-Protocol-Version: 2025-06-18`

The protocol version sent by the client **SHOULD** be the one [negotiated during
initialization](/specification/2025-06-18/basic/lifecycle#version-negotiation).

For backwards compatibility, if the server does *not* receive an `MCP-Protocol-Version`
header, and has no other way to identify the version - for example, by relying on the
protocol version negotiated during initialization - the server **SHOULD** assume protocol
version `2025-06-18`.

If the server receives a request with an invalid or unsupported
`MCP-Protocol-Version`, it **MUST** respond with `400 Bad Request`.

### Backwards Compatibility

Clients and servers can maintain backwards compatibility with the deprecated [HTTP+SSE
transport](/specification/2024-11-05/basic/transports#http-with-sse) (from
protocol version 2024-11-05) as follows:

**Servers** wanting to support older clients should:

* Continue to host both the SSE and POST endpoints of the old transport, alongside the
  new "MCP endpoint" defined for the Streamable HTTP transport.
  * It is also possible to combine the old POST endpoint and the new MCP endpoint, but
    this may introduce unneeded complexity.

**Clients** wanting to support older servers should:

1. Accept an MCP server URL from the user, which may point to either a server using the
   old transport or the new transport.
2. Attempt to POST an `InitializeRequest` to the server URL, with an `Accept` header as
   defined above:
   * If it succeeds, the client can assume this is a server supporting the new Streamable
     HTTP transport.
   * If it fails with an HTTP 4xx status code (e.g., 405 Method Not Allowed or 404 Not
     Found):
     * Issue a GET request to the server URL, expecting that this will open an SSE stream
       and return an `endpoint` event as the first event.
     * When the `endpoint` event arrives, the client can assume this is a server running
       the old HTTP+SSE transport, and should use that transport for all subsequent
       communication.

## Custom Transports

Clients and servers **MAY** implement additional custom transport mechanisms to suit
their specific needs. The protocol is transport-agnostic and can be implemented over any
communication channel that supports bidirectional message exchange.

Implementers who choose to support custom transports **MUST** ensure they preserve the
JSON-RPC message format and lifecycle requirements defined by MCP. Custom transports
**SHOULD** document their specific connection establishment and message exchange patterns
to aid interoperability.


# Cancellation
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/utilities/cancellation



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) supports optional cancellation of in-progress requests
through notification messages. Either side can send a cancellation notification to
indicate that a previously-issued request should be terminated.

## Cancellation Flow

When a party wants to cancel an in-progress request, it sends a `notifications/cancelled`
notification containing:

* The ID of the request to cancel
* An optional reason string that can be logged or displayed

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/cancelled",
  "params": {
    "requestId": "123",
    "reason": "User requested cancellation"
  }
}
```

## Behavior Requirements

1. Cancellation notifications **MUST** only reference requests that:
   * Were previously issued in the same direction
   * Are believed to still be in-progress
2. The `initialize` request **MUST NOT** be cancelled by clients
3. Receivers of cancellation notifications **SHOULD**:
   * Stop processing the cancelled request
   * Free associated resources
   * Not send a response for the cancelled request
4. Receivers **MAY** ignore cancellation notifications if:
   * The referenced request is unknown
   * Processing has already completed
   * The request cannot be cancelled
5. The sender of the cancellation notification **SHOULD** ignore any response to the
   request that arrives afterward

## Timing Considerations

Due to network latency, cancellation notifications may arrive after request processing
has completed, and potentially after a response has already been sent.

Both parties **MUST** handle these race conditions gracefully:

```mermaid
sequenceDiagram
   participant Client
   participant Server

   Client->>Server: Request (ID: 123)
   Note over Server: Processing starts
   Client--)Server: notifications/cancelled (ID: 123)
   alt
      Note over Server: Processing may have<br/>completed before<br/>cancellation arrives
   else If not completed
      Note over Server: Stop processing
   end
```

## Implementation Notes

* Both parties **SHOULD** log cancellation reasons for debugging
* Application UIs **SHOULD** indicate when cancellation is requested

## Error Handling

Invalid cancellation notifications **SHOULD** be ignored:

* Unknown request IDs
* Already completed requests
* Malformed notifications

This maintains the "fire and forget" nature of notifications while allowing for race
conditions in asynchronous communication.


# Ping
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/utilities/ping



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol includes an optional ping mechanism that allows either party
to verify that their counterpart is still responsive and the connection is alive.

## Overview

The ping functionality is implemented through a simple request/response pattern. Either
the client or server can initiate a ping by sending a `ping` request.

## Message Format

A ping request is a standard JSON-RPC request with no parameters:

```json
{
  "jsonrpc": "2.0",
  "id": "123",
  "method": "ping"
}
```

## Behavior Requirements

1. The receiver **MUST** respond promptly with an empty response:

```json
{
  "jsonrpc": "2.0",
  "id": "123",
  "result": {}
}
```

2. If no response is received within a reasonable timeout period, the sender **MAY**:
   * Consider the connection stale
   * Terminate the connection
   * Attempt reconnection procedures

## Usage Patterns

```mermaid
sequenceDiagram
    participant Sender
    participant Receiver

    Sender->>Receiver: ping request
    Receiver->>Sender: empty response
```

## Implementation Considerations

* Implementations **SHOULD** periodically issue pings to detect connection health
* The frequency of pings **SHOULD** be configurable
* Timeouts **SHOULD** be appropriate for the network environment
* Excessive pinging **SHOULD** be avoided to reduce network overhead

## Error Handling

* Timeouts **SHOULD** be treated as connection failures
* Multiple failed pings **MAY** trigger connection reset
* Implementations **SHOULD** log ping failures for diagnostics


# Progress
Source: https://modelcontextprotocol.io/specification/2025-06-18/basic/utilities/progress



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) supports optional progress tracking for long-running
operations through notification messages. Either side can send progress notifications to
provide updates about operation status.

## Progress Flow

When a party wants to *receive* progress updates for a request, it includes a
`progressToken` in the request metadata.

* Progress tokens **MUST** be a string or integer value
* Progress tokens can be chosen by the sender using any means, but **MUST** be unique
  across all active requests.

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "some_method",
  "params": {
    "_meta": {
      "progressToken": "abc123"
    }
  }
}
```

The receiver **MAY** then send progress notifications containing:

* The original progress token
* The current progress value so far
* An optional "total" value
* An optional "message" value

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/progress",
  "params": {
    "progressToken": "abc123",
    "progress": 50,
    "total": 100,
    "message": "Reticulating splines..."
  }
}
```

* The `progress` value **MUST** increase with each notification, even if the total is
  unknown.
* The `progress` and the `total` values **MAY** be floating point.
* The `message` field **SHOULD** provide relevant human readable progress information.

## Behavior Requirements

1. Progress notifications **MUST** only reference tokens that:

   * Were provided in an active request
   * Are associated with an in-progress operation

2. Receivers of progress requests **MAY**:
   * Choose not to send any progress notifications
   * Send notifications at whatever frequency they deem appropriate
   * Omit the total value if unknown

```mermaid
sequenceDiagram
    participant Sender
    participant Receiver

    Note over Sender,Receiver: Request with progress token
    Sender->>Receiver: Method request with progressToken

    Note over Sender,Receiver: Progress updates
    loop Progress Updates
        Receiver-->>Sender: Progress notification (0.2/1.0)
        Receiver-->>Sender: Progress notification (0.6/1.0)
        Receiver-->>Sender: Progress notification (1.0/1.0)
    end

    Note over Sender,Receiver: Operation complete
    Receiver->>Sender: Method response
```

## Implementation Notes

* Senders and receivers **SHOULD** track active progress tokens
* Both parties **SHOULD** implement rate limiting to prevent flooding
* Progress notifications **MUST** stop after completion


# Key Changes
Source: https://modelcontextprotocol.io/specification/2025-06-18/changelog



<div id="enable-section-numbers" />

This document lists changes made to the Model Context Protocol (MCP) specification since
the previous revision, [2025-03-26](/specification/2025-03-26).

## Major changes

1. Remove support for JSON-RPC **[batching](https://www.jsonrpc.org/specification#batch)**
   (PR [#416](https://github.com/modelcontextprotocol/specification/pull/416))
2. Add support for [structured tool output](/specification/2025-06-18/server/tools#structured-content)
   (PR [#371](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/371))
3. Classify MCP servers as [OAuth Resource Servers](/specification/2025-06-18/basic/authorization#authorization-server-discovery),
   adding protected resource metadata to discover the corresponding Authorization server.
   (PR [#338](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/338))
4. Require MCP clients to implement Resource Indicators as described in [RFC 8707](https://www.rfc-editor.org/rfc/rfc8707.html) to prevent
   malicious servers from obtaining access tokens.
   (PR [#734](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/734))
5. Clarify [security considerations](/specification/2025-06-18/basic/authorization#security-considerations) and best practices
   in the authorization spec and in a new [security best practices page](/specification/2025-06-18/basic/security_best_practices).
6. Add support for **[elicitation](/specification/2025-06-18/client/elicitation)**, enabling servers to request additional
   information from users during interactions.
   (PR [#382](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/382))
7. Add support for **[resource links](/specification/2025-06-18/server/tools#resource-links)** in
   tool call results. (PR [#603](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/603))
8. Require [negotiated protocol version to be specified](/specification/2025-06-18/basic/transports#protocol-version-header)
   via `MCP-Protocol-Version` header in subsequent requests when using HTTP (PR [#548](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/548)).
9. Change **SHOULD** to **MUST** in [Lifecycle Operation](/specification/2025-06-18/basic/lifecycle#operation)

## Other schema changes

1. Add `_meta` field to additional interface types (PR [#710](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/710)),
   and specify [proper usage](/specification/2025-06-18/basic#meta).
2. Add `context` field to `CompletionRequest`, providing for completion requests to include
   previously-resolved variables (PR [#598](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/598)).
3. Add `title` field for human-friendly display names, so that `name` can be used as a programmatic
   identifier (PR [#663](https://github.com/modelcontextprotocol/modelcontextprotocol/pull/663))

## Full changelog

For a complete list of all changes that have been made since the last protocol revision,
[see GitHub](https://github.com/modelcontextprotocol/specification/compare/2025-03-26...2025-06-18).


# Elicitation
Source: https://modelcontextprotocol.io/specification/2025-06-18/client/elicitation



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

<Note>
  Elicitation is newly introduced in this version of the MCP specification and its design may evolve in future protocol versions.
</Note>

The Model Context Protocol (MCP) provides a standardized way for servers to request additional
information from users through the client during interactions. This flow allows clients to
maintain control over user interactions and data sharing while enabling servers to gather
necessary information dynamically.
Servers request structured data from users with JSON schemas to validate responses.

## User Interaction Model

Elicitation in MCP allows servers to implement interactive workflows by enabling user input
requests to occur *nested* inside other MCP server features.

Implementations are free to expose elicitation through any interface pattern that suits
their needs—the protocol itself does not mandate any specific user interaction
model.

<Warning>
  For trust & safety and security:

  * Servers **MUST NOT** use elicitation to request sensitive information.

  Applications **SHOULD**:

  * Provide UI that makes it clear which server is requesting information
  * Allow users to review and modify their responses before sending
  * Respect user privacy and provide clear reject and cancel options
</Warning>

## Capabilities

Clients that support elicitation **MUST** declare the `elicitation` capability during
[initialization](/specification/2025-06-18/basic/lifecycle#initialization):

```json
{
  "capabilities": {
    "elicitation": {}
  }
}
```

## Protocol Messages

### Creating Elicitation Requests

To request information from a user, servers send an `elicitation/create` request:

#### Simple Text Request

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "elicitation/create",
  "params": {
    "message": "Please provide your GitHub username",
    "requestedSchema": {
      "type": "object",
      "properties": {
        "name": {
          "type": "string"
        }
      },
      "required": ["name"]
    }
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "action": "accept",
    "content": {
      "name": "octocat"
    }
  }
}
```

#### Structured Data Request

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "elicitation/create",
  "params": {
    "message": "Please provide your contact information",
    "requestedSchema": {
      "type": "object",
      "properties": {
        "name": {
          "type": "string",
          "description": "Your full name"
        },
        "email": {
          "type": "string",
          "format": "email",
          "description": "Your email address"
        },
        "age": {
          "type": "number",
          "minimum": 18,
          "description": "Your age"
        }
      },
      "required": ["name", "email"]
    }
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "action": "accept",
    "content": {
      "name": "Monalisa Octocat",
      "email": "octocat@github.com",
      "age": 30
    }
  }
}
```

**Reject Response Example:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "action": "reject"
  }
}
```

**Cancel Response Example:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "action": "cancel"
  }
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant User
    participant Client
    participant Server

    Note over Server,Client: Server initiates elicitation
    Server->>Client: elicitation/create

    Note over Client,User: Human interaction
    Client->>User: Present elicitation UI
    User-->>Client: Provide requested information

    Note over Server,Client: Complete request
    Client-->>Server: Return user response

    Note over Server: Continue processing with new information
```

## Request Schema

The `requestedSchema` field allows servers to define the structure of the expected response using a restricted subset of JSON Schema. To simplify implementation for clients, elicitation schemas are limited to flat objects with primitive properties only:

```json
"requestedSchema": {
  "type": "object",
  "properties": {
    "propertyName": {
      "type": "string",
      "title": "Display Name",
      "description": "Description of the property"
    },
    "anotherProperty": {
      "type": "number",
      "minimum": 0,
      "maximum": 100
    }
  },
  "required": ["propertyName"]
}
```

### Supported Schema Types

The schema is restricted to these primitive types:

1. **String Schema**

   ```json
   {
     "type": "string",
     "title": "Display Name",
     "description": "Description text",
     "minLength": 3,
     "maxLength": 50,
     "format": "email" // Supported: "email", "uri", "date", "date-time"
   }
   ```

   Supported formats: `email`, `uri`, `date`, `date-time`

2. **Number Schema**

   ```json
   {
     "type": "number", // or "integer"
     "title": "Display Name",
     "description": "Description text",
     "minimum": 0,
     "maximum": 100
   }
   ```

3. **Boolean Schema**

   ```json
   {
     "type": "boolean",
     "title": "Display Name",
     "description": "Description text",
     "default": false
   }
   ```

4. **Enum Schema**
   ```json
   {
     "type": "string",
     "title": "Display Name",
     "description": "Description text",
     "enum": ["option1", "option2", "option3"],
     "enumNames": ["Option 1", "Option 2", "Option 3"]
   }
   ```

Clients can use this schema to:

1. Generate appropriate input forms
2. Validate user input before sending
3. Provide better guidance to users

Note that complex nested structures, arrays of objects, and other advanced JSON Schema features are intentionally not supported to simplify client implementation.

## Response Actions

Elicitation responses use a three-action model to clearly distinguish between different user actions:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "action": "accept", // or "reject" or "cancel"
    "content": {
      "propertyName": "value",
      "anotherProperty": 42
    }
  }
}
```

The three response actions are:

1. **Accept** (`action: "accept"`): User explicitly approved and submitted with data

   * The `content` field contains the submitted data matching the requested schema
   * Example: User clicked "Submit", "OK", "Confirm", etc.

2. **Reject** (`action: "reject"`): User explicitly rejected the request

   * The `content` field is typically omitted
   * Example: User clicked "Reject", "Decline", "No", etc.

3. **Cancel** (`action: "cancel"`): User dismissed without making an explicit choice
   * The `content` field is typically omitted
   * Example: User closed the dialog, clicked outside, pressed Escape, etc.

Servers should handle each state appropriately:

* **Accept**: Process the submitted data
* **Reject**: Handle explicit rejection (e.g., offer alternatives)
* **Cancel**: Handle dismissal (e.g., prompt again later)

## Security Considerations

1. Servers **MUST NOT** request sensitive information through elicitation
2. Clients **SHOULD** implement user approval controls
3. Both parties **SHOULD** validate elicitation content against the provided schema
4. Clients **SHOULD** provide clear indication of which server is requesting information
5. Clients **SHOULD** allow users to reject elicitation requests at any time
6. Clients **SHOULD** implement rate limiting
7. Clients **SHOULD** present elicitation requests in a way that makes it clear what information is being requested and why


# Roots
Source: https://modelcontextprotocol.io/specification/2025-06-18/client/roots



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) provides a standardized way for clients to expose
filesystem "roots" to servers. Roots define the boundaries of where servers can operate
within the filesystem, allowing them to understand which directories and files they have
access to. Servers can request the list of roots from supporting clients and receive
notifications when that list changes.

## User Interaction Model

Roots in MCP are typically exposed through workspace or project configuration interfaces.

For example, implementations could offer a workspace/project picker that allows users to
select directories and files the server should have access to. This can be combined with
automatic workspace detection from version control systems or project files.

However, implementations are free to expose roots through any interface pattern that
suits their needs—the protocol itself does not mandate any specific user
interaction model.

## Capabilities

Clients that support roots **MUST** declare the `roots` capability during
[initialization](/specification/2025-06-18/basic/lifecycle#initialization):

```json
{
  "capabilities": {
    "roots": {
      "listChanged": true
    }
  }
}
```

`listChanged` indicates whether the client will emit notifications when the list of roots
changes.

## Protocol Messages

### Listing Roots

To retrieve roots, servers send a `roots/list` request:

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "roots/list"
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "roots": [
      {
        "uri": "file:///home/user/projects/myproject",
        "name": "My Project"
      }
    ]
  }
}
```

### Root List Changes

When roots change, clients that support `listChanged` **MUST** send a notification:

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/roots/list_changed"
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant Server
    participant Client

    Note over Server,Client: Discovery
    Server->>Client: roots/list
    Client-->>Server: Available roots

    Note over Server,Client: Changes
    Client--)Server: notifications/roots/list_changed
    Server->>Client: roots/list
    Client-->>Server: Updated roots
```

## Data Types

### Root

A root definition includes:

* `uri`: Unique identifier for the root. This **MUST** be a `file://` URI in the current
  specification.
* `name`: Optional human-readable name for display purposes.

Example roots for different use cases:

#### Project Directory

```json
{
  "uri": "file:///home/user/projects/myproject",
  "name": "My Project"
}
```

#### Multiple Repositories

```json
[
  {
    "uri": "file:///home/user/repos/frontend",
    "name": "Frontend Repository"
  },
  {
    "uri": "file:///home/user/repos/backend",
    "name": "Backend Repository"
  }
]
```

## Error Handling

Clients **SHOULD** return standard JSON-RPC errors for common failure cases:

* Client does not support roots: `-32601` (Method not found)
* Internal errors: `-32603`

Example error:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "error": {
    "code": -32601,
    "message": "Roots not supported",
    "data": {
      "reason": "Client does not have roots capability"
    }
  }
}
```

## Security Considerations

1. Clients **MUST**:

   * Only expose roots with appropriate permissions
   * Validate all root URIs to prevent path traversal
   * Implement proper access controls
   * Monitor root accessibility

2. Servers **SHOULD**:
   * Handle cases where roots become unavailable
   * Respect root boundaries during operations
   * Validate all paths against provided roots

## Implementation Guidelines

1. Clients **SHOULD**:

   * Prompt users for consent before exposing roots to servers
   * Provide clear user interfaces for root management
   * Validate root accessibility before exposing
   * Monitor for root changes

2. Servers **SHOULD**:
   * Check for roots capability before usage
   * Handle root list changes gracefully
   * Respect root boundaries in operations
   * Cache root information appropriately


# Sampling
Source: https://modelcontextprotocol.io/specification/2025-06-18/client/sampling



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) provides a standardized way for servers to request LLM
sampling ("completions" or "generations") from language models via clients. This flow
allows clients to maintain control over model access, selection, and permissions while
enabling servers to leverage AI capabilities—with no server API keys necessary.
Servers can request text, audio, or image-based interactions and optionally include
context from MCP servers in their prompts.

## User Interaction Model

Sampling in MCP allows servers to implement agentic behaviors, by enabling LLM calls to
occur *nested* inside other MCP server features.

Implementations are free to expose sampling through any interface pattern that suits
their needs—the protocol itself does not mandate any specific user interaction
model.

<Warning>
  For trust & safety and security, there **SHOULD** always
  be a human in the loop with the ability to deny sampling requests.

  Applications **SHOULD**:

  * Provide UI that makes it easy and intuitive to review sampling requests
  * Allow users to view and edit prompts before sending
  * Present generated responses for review before delivery
</Warning>

## Capabilities

Clients that support sampling **MUST** declare the `sampling` capability during
[initialization](/specification/2025-06-18/basic/lifecycle#initialization):

```json
{
  "capabilities": {
    "sampling": {}
  }
}
```

## Protocol Messages

### Creating Messages

To request a language model generation, servers send a `sampling/createMessage` request:

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "sampling/createMessage",
  "params": {
    "messages": [
      {
        "role": "user",
        "content": {
          "type": "text",
          "text": "What is the capital of France?"
        }
      }
    ],
    "modelPreferences": {
      "hints": [
        {
          "name": "claude-3-sonnet"
        }
      ],
      "intelligencePriority": 0.8,
      "speedPriority": 0.5
    },
    "systemPrompt": "You are a helpful assistant.",
    "maxTokens": 100
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "role": "assistant",
    "content": {
      "type": "text",
      "text": "The capital of France is Paris."
    },
    "model": "claude-3-sonnet-20240307",
    "stopReason": "endTurn"
  }
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant Server
    participant Client
    participant User
    participant LLM

    Note over Server,Client: Server initiates sampling
    Server->>Client: sampling/createMessage

    Note over Client,User: Human-in-the-loop review
    Client->>User: Present request for approval
    User-->>Client: Review and approve/modify

    Note over Client,LLM: Model interaction
    Client->>LLM: Forward approved request
    LLM-->>Client: Return generation

    Note over Client,User: Response review
    Client->>User: Present response for approval
    User-->>Client: Review and approve/modify

    Note over Server,Client: Complete request
    Client-->>Server: Return approved response
```

## Data Types

### Messages

Sampling messages can contain:

#### Text Content

```json
{
  "type": "text",
  "text": "The message content"
}
```

#### Image Content

```json
{
  "type": "image",
  "data": "base64-encoded-image-data",
  "mimeType": "image/jpeg"
}
```

#### Audio Content

```json
{
  "type": "audio",
  "data": "base64-encoded-audio-data",
  "mimeType": "audio/wav"
}
```

### Model Preferences

Model selection in MCP requires careful abstraction since servers and clients may use
different AI providers with distinct model offerings. A server cannot simply request a
specific model by name since the client may not have access to that exact model or may
prefer to use a different provider's equivalent model.

To solve this, MCP implements a preference system that combines abstract capability
priorities with optional model hints:

#### Capability Priorities

Servers express their needs through three normalized priority values (0-1):

* `costPriority`: How important is minimizing costs? Higher values prefer cheaper models.
* `speedPriority`: How important is low latency? Higher values prefer faster models.
* `intelligencePriority`: How important are advanced capabilities? Higher values prefer
  more capable models.

#### Model Hints

While priorities help select models based on characteristics, `hints` allow servers to
suggest specific models or model families:

* Hints are treated as substrings that can match model names flexibly
* Multiple hints are evaluated in order of preference
* Clients **MAY** map hints to equivalent models from different providers
* Hints are advisory—clients make final model selection

For example:

```json
{
  "hints": [
    { "name": "claude-3-sonnet" }, // Prefer Sonnet-class models
    { "name": "claude" } // Fall back to any Claude model
  ],
  "costPriority": 0.3, // Cost is less important
  "speedPriority": 0.8, // Speed is very important
  "intelligencePriority": 0.5 // Moderate capability needs
}
```

The client processes these preferences to select an appropriate model from its available
options. For instance, if the client doesn't have access to Claude models but has Gemini,
it might map the sonnet hint to `gemini-1.5-pro` based on similar capabilities.

## Error Handling

Clients **SHOULD** return errors for common failure cases:

Example error:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "error": {
    "code": -1,
    "message": "User rejected sampling request"
  }
}
```

## Security Considerations

1. Clients **SHOULD** implement user approval controls
2. Both parties **SHOULD** validate message content
3. Clients **SHOULD** respect model preference hints
4. Clients **SHOULD** implement rate limiting
5. Both parties **MUST** handle sensitive data appropriately


# Specification
Source: https://modelcontextprotocol.io/specification/2025-06-18/index



<div id="enable-section-numbers" />

[Model Context Protocol](https://modelcontextprotocol.io) (MCP) is an open protocol that
enables seamless integration between LLM applications and external data sources and
tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating
custom AI workflows, MCP provides a standardized way to connect LLMs with the context
they need.

This specification defines the authoritative protocol requirements, based on the
TypeScript schema in
[schema.ts](https://github.com/modelcontextprotocol/specification/blob/main/schema/2025-06-18/schema.ts).

For implementation guides and examples, visit
[modelcontextprotocol.io](https://modelcontextprotocol.io).

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD
NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be
interpreted as described in [BCP 14](https://datatracker.ietf.org/doc/html/bcp14)
\[[RFC2119](https://datatracker.ietf.org/doc/html/rfc2119)]
\[[RFC8174](https://datatracker.ietf.org/doc/html/rfc8174)] when, and only when, they
appear in all capitals, as shown here.

## Overview

MCP provides a standardized way for applications to:

* Share contextual information with language models
* Expose tools and capabilities to AI systems
* Build composable integrations and workflows

The protocol uses [JSON-RPC](https://www.jsonrpc.org/) 2.0 messages to establish
communication between:

* **Hosts**: LLM applications that initiate connections
* **Clients**: Connectors within the host application
* **Servers**: Services that provide context and capabilities

MCP takes some inspiration from the
[Language Server Protocol](https://microsoft.github.io/language-server-protocol/), which
standardizes how to add support for programming languages across a whole ecosystem of
development tools. In a similar way, MCP standardizes how to integrate additional context
and tools into the ecosystem of AI applications.

## Key Details

### Base Protocol

* [JSON-RPC](https://www.jsonrpc.org/) message format
* Stateful connections
* Server and client capability negotiation

### Features

Servers offer any of the following features to clients:

* **Resources**: Context and data, for the user or the AI model to use
* **Prompts**: Templated messages and workflows for users
* **Tools**: Functions for the AI model to execute

Clients may offer the following features to servers:

* **Sampling**: Server-initiated agentic behaviors and recursive LLM interactions
* **Roots**: Server-initiated inquiries into uri or filesystem boundaries to operate in
* **Elicitation**: Server-initiated requests for additional information from users

### Additional Utilities

* Configuration
* Progress tracking
* Cancellation
* Error reporting
* Logging

## Security and Trust & Safety

The Model Context Protocol enables powerful capabilities through arbitrary data access
and code execution paths. With this power comes important security and trust
considerations that all implementors must carefully address.

### Key Principles

1. **User Consent and Control**

   * Users must explicitly consent to and understand all data access and operations
   * Users must retain control over what data is shared and what actions are taken
   * Implementors should provide clear UIs for reviewing and authorizing activities

2. **Data Privacy**

   * Hosts must obtain explicit user consent before exposing user data to servers
   * Hosts must not transmit resource data elsewhere without user consent
   * User data should be protected with appropriate access controls

3. **Tool Safety**

   * Tools represent arbitrary code execution and must be treated with appropriate
     caution.
     * In particular, descriptions of tool behavior such as annotations should be
       considered untrusted, unless obtained from a trusted server.
   * Hosts must obtain explicit user consent before invoking any tool
   * Users should understand what each tool does before authorizing its use

4. **LLM Sampling Controls**
   * Users must explicitly approve any LLM sampling requests
   * Users should control:
     * Whether sampling occurs at all
     * The actual prompt that will be sent
     * What results the server can see
   * The protocol intentionally limits server visibility into prompts

### Implementation Guidelines

While MCP itself cannot enforce these security principles at the protocol level,
implementors **SHOULD**:

1. Build robust consent and authorization flows into their applications
2. Provide clear documentation of security implications
3. Implement appropriate access controls and data protections
4. Follow security best practices in their integrations
5. Consider privacy implications in their feature designs

## Learn More

Explore the detailed specification for each protocol component:

<CardGroup cols={5}>
  <Card title="Architecture" icon="sitemap" href="/specification/2025-06-18/architecture" />

  <Card title="Base Protocol" icon="code" href="/specification/2025-06-18/basic" />

  <Card title="Server Features" icon="server" href="/specification/2025-06-18/server" />

  <Card title="Client Features" icon="user" href="/specification/2025-06-18/client" />

  <Card title="Contributing" icon="pencil" href="/development/contributing" />
</CardGroup>


# Overview
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/index



<Info>**Protocol Revision**: 2025-06-18</Info>

Servers provide the fundamental building blocks for adding context to language models via
MCP. These primitives enable rich interactions between clients, servers, and language
models:

* **Prompts**: Pre-defined templates or instructions that guide language model
  interactions
* **Resources**: Structured data or content that provides additional context to the model
* **Tools**: Executable functions that allow models to perform actions or retrieve
  information

Each primitive can be summarized in the following control hierarchy:

| Primitive | Control                | Description                                        | Example                         |
| --------- | ---------------------- | -------------------------------------------------- | ------------------------------- |
| Prompts   | User-controlled        | Interactive templates invoked by user choice       | Slash commands, menu options    |
| Resources | Application-controlled | Contextual data attached and managed by the client | File contents, git history      |
| Tools     | Model-controlled       | Functions exposed to the LLM to take actions       | API POST requests, file writing |

Explore these key primitives in more detail below:

<CardGroup cols={3}>
  <Card title="Prompts" icon="message" href="/specification/2025-06-18/server/prompts" />

  <Card title="Resources" icon="file-lines" href="/specification/2025-06-18/server/resources" />

  <Card title="Tools" icon="wrench" href="/specification/2025-06-18/server/tools" />
</CardGroup>


# Prompts
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/prompts



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) provides a standardized way for servers to expose prompt
templates to clients. Prompts allow servers to provide structured messages and
instructions for interacting with language models. Clients can discover available
prompts, retrieve their contents, and provide arguments to customize them.

## User Interaction Model

Prompts are designed to be **user-controlled**, meaning they are exposed from servers to
clients with the intention of the user being able to explicitly select them for use.

Typically, prompts would be triggered through user-initiated commands in the user
interface, which allows users to naturally discover and invoke available prompts.

For example, as slash commands:

![Example of prompt exposed as slash command](https://mintlify.s3.us-west-1.amazonaws.com/mcp/specification/2025-06-18/server/slash-command.png)

However, implementors are free to expose prompts through any interface pattern that suits
their needs—the protocol itself does not mandate any specific user interaction
model.

## Capabilities

Servers that support prompts **MUST** declare the `prompts` capability during
[initialization](/specification/2025-06-18/basic/lifecycle#initialization):

```json
{
  "capabilities": {
    "prompts": {
      "listChanged": true
    }
  }
}
```

`listChanged` indicates whether the server will emit notifications when the list of
available prompts changes.

## Protocol Messages

### Listing Prompts

To retrieve available prompts, clients send a `prompts/list` request. This operation
supports [pagination](/specification/2025-06-18/server/utilities/pagination).

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "prompts/list",
  "params": {
    "cursor": "optional-cursor-value"
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "prompts": [
      {
        "name": "code_review",
        "title": "Request Code Review",
        "description": "Asks the LLM to analyze code quality and suggest improvements",
        "arguments": [
          {
            "name": "code",
            "description": "The code to review",
            "required": true
          }
        ]
      }
    ],
    "nextCursor": "next-page-cursor"
  }
}
```

### Getting a Prompt

To retrieve a specific prompt, clients send a `prompts/get` request. Arguments may be
auto-completed through [the completion API](/specification/2025-06-18/server/utilities/completion).

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "prompts/get",
  "params": {
    "name": "code_review",
    "arguments": {
      "code": "def hello():\n    print('world')"
    }
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "description": "Code review prompt",
    "messages": [
      {
        "role": "user",
        "content": {
          "type": "text",
          "text": "Please review this Python code:\ndef hello():\n    print('world')"
        }
      }
    ]
  }
}
```

### List Changed Notification

When the list of available prompts changes, servers that declared the `listChanged`
capability **SHOULD** send a notification:

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/prompts/list_changed"
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Note over Client,Server: Discovery
    Client->>Server: prompts/list
    Server-->>Client: List of prompts

    Note over Client,Server: Usage
    Client->>Server: prompts/get
    Server-->>Client: Prompt content

    opt listChanged
      Note over Client,Server: Changes
      Server--)Client: prompts/list_changed
      Client->>Server: prompts/list
      Server-->>Client: Updated prompts
    end
```

## Data Types

### Prompt

A prompt definition includes:

* `name`: Unique identifier for the prompt
* `title`: Optional human-readable name of the prompt for display purposes.
* `description`: Optional human-readable description
* `arguments`: Optional list of arguments for customization

### PromptMessage

Messages in a prompt can contain:

* `role`: Either "user" or "assistant" to indicate the speaker
* `content`: One of the following content types:

#### Text Content

Text content represents plain text messages:

```json
{
  "type": "text",
  "text": "The text content of the message"
}
```

This is the most common content type used for natural language interactions.

#### Image Content

Image content allows including visual information in messages:

```json
{
  "type": "image",
  "data": "base64-encoded-image-data",
  "mimeType": "image/png"
}
```

The image data **MUST** be base64-encoded and include a valid MIME type. This enables
multi-modal interactions where visual context is important.

#### Audio Content

Audio content allows including audio information in messages:

```json
{
  "type": "audio",
  "data": "base64-encoded-audio-data",
  "mimeType": "audio/wav"
}
```

The audio data MUST be base64-encoded and include a valid MIME type. This enables
multi-modal interactions where audio context is important.

#### Embedded Resources

Embedded resources allow referencing server-side resources directly in messages:

```json
{
  "type": "resource",
  "resource": {
    "uri": "resource://example",
    "name": "example",
    "title": "My Example Resource",
    "mimeType": "text/plain",
    "text": "Resource content"
  }
}
```

Resources can contain either text or binary (blob) data and **MUST** include:

* A valid resource URI
* The appropriate MIME type
* Either text content or base64-encoded blob data

Embedded resources enable prompts to seamlessly incorporate server-managed content like
documentation, code samples, or other reference materials directly into the conversation
flow.

## Error Handling

Servers **SHOULD** return standard JSON-RPC errors for common failure cases:

* Invalid prompt name: `-32602` (Invalid params)
* Missing required arguments: `-32602` (Invalid params)
* Internal errors: `-32603` (Internal error)

## Implementation Considerations

1. Servers **SHOULD** validate prompt arguments before processing
2. Clients **SHOULD** handle pagination for large prompt lists
3. Both parties **SHOULD** respect capability negotiation

## Security

Implementations **MUST** carefully validate all prompt inputs and outputs to prevent
injection attacks or unauthorized access to resources.


# Resources
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/resources



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) provides a standardized way for servers to expose
resources to clients. Resources allow servers to share data that provides context to
language models, such as files, database schemas, or application-specific information.
Each resource is uniquely identified by a
[URI](https://datatracker.ietf.org/doc/html/rfc3986).

## User Interaction Model

Resources in MCP are designed to be **application-driven**, with host applications
determining how to incorporate context based on their needs.

For example, applications could:

* Expose resources through UI elements for explicit selection, in a tree or list view
* Allow the user to search through and filter available resources
* Implement automatic context inclusion, based on heuristics or the AI model's selection

![Example of resource context picker](https://mintlify.s3.us-west-1.amazonaws.com/mcp/specification/2025-06-18/server/resource-picker.png)

However, implementations are free to expose resources through any interface pattern that
suits their needs—the protocol itself does not mandate any specific user
interaction model.

## Capabilities

Servers that support resources **MUST** declare the `resources` capability:

```json
{
  "capabilities": {
    "resources": {
      "subscribe": true,
      "listChanged": true
    }
  }
}
```

The capability supports two optional features:

* `subscribe`: whether the client can subscribe to be notified of changes to individual
  resources.
* `listChanged`: whether the server will emit notifications when the list of available
  resources changes.

Both `subscribe` and `listChanged` are optional—servers can support neither,
either, or both:

```json
{
  "capabilities": {
    "resources": {} // Neither feature supported
  }
}
```

```json
{
  "capabilities": {
    "resources": {
      "subscribe": true // Only subscriptions supported
    }
  }
}
```

```json
{
  "capabilities": {
    "resources": {
      "listChanged": true // Only list change notifications supported
    }
  }
}
```

## Protocol Messages

### Listing Resources

To discover available resources, clients send a `resources/list` request. This operation
supports [pagination](/specification/2025-06-18/server/utilities/pagination).

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "resources/list",
  "params": {
    "cursor": "optional-cursor-value"
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "resources": [
      {
        "uri": "file:///project/src/main.rs",
        "name": "main.rs",
        "title": "Rust Software Application Main File",
        "description": "Primary application entry point",
        "mimeType": "text/x-rust"
      }
    ],
    "nextCursor": "next-page-cursor"
  }
}
```

### Reading Resources

To retrieve resource contents, clients send a `resources/read` request:

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "resources/read",
  "params": {
    "uri": "file:///project/src/main.rs"
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "contents": [
      {
        "uri": "file:///project/src/main.rs",
        "name": "main.rs",
        "title": "Rust Software Application Main File",
        "mimeType": "text/x-rust",
        "text": "fn main() {\n    println!(\"Hello world!\");\n}"
      }
    ]
  }
}
```

### Resource Templates

Resource templates allow servers to expose parameterized resources using
[URI templates](https://datatracker.ietf.org/doc/html/rfc6570). Arguments may be
auto-completed through [the completion API](/specification/2025-06-18/server/utilities/completion).

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 3,
  "method": "resources/templates/list"
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 3,
  "result": {
    "resourceTemplates": [
      {
        "uriTemplate": "file:///{path}",
        "name": "Project Files",
        "title": "📁 Project Files",
        "description": "Access files in the project directory",
        "mimeType": "application/octet-stream"
      }
    ]
  }
}
```

### List Changed Notification

When the list of available resources changes, servers that declared the `listChanged`
capability **SHOULD** send a notification:

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/resources/list_changed"
}
```

### Subscriptions

The protocol supports optional subscriptions to resource changes. Clients can subscribe
to specific resources and receive notifications when they change:

**Subscribe Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 4,
  "method": "resources/subscribe",
  "params": {
    "uri": "file:///project/src/main.rs"
  }
}
```

**Update Notification:**

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/resources/updated",
  "params": {
    "uri": "file:///project/src/main.rs",
    "title": "Rust Software Application Main File"
  }
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Note over Client,Server: Resource Discovery
    Client->>Server: resources/list
    Server-->>Client: List of resources

    Note over Client,Server: Resource Access
    Client->>Server: resources/read
    Server-->>Client: Resource contents

    Note over Client,Server: Subscriptions
    Client->>Server: resources/subscribe
    Server-->>Client: Subscription confirmed

    Note over Client,Server: Updates
    Server--)Client: notifications/resources/updated
    Client->>Server: resources/read
    Server-->>Client: Updated contents
```

## Data Types

### Resource

A resource definition includes:

* `uri`: Unique identifier for the resource
* `name`: The name of the resource.
* `title`: Optional human-readable name of the resource for display purposes.
* `description`: Optional description
* `mimeType`: Optional MIME type
* `size`: Optional size in bytes

### Resource Contents

Resources can contain either text or binary data:

#### Text Content

```json
{
  "uri": "file:///example.txt",
  "name": "example.txt",
  "title": "Example Text File",
  "mimeType": "text/plain",
  "text": "Resource content"
}
```

#### Binary Content

```json
{
  "uri": "file:///example.png",
  "name": "example.png",
  "title": "Example Image",
  "mimeType": "image/png",
  "blob": "base64-encoded-data"
}
```

## Common URI Schemes

The protocol defines several standard URI schemes. This list not
exhaustive—implementations are always free to use additional, custom URI schemes.

### https\://

Used to represent a resource available on the web.

Servers **SHOULD** use this scheme only when the client is able to fetch and load the
resource directly from the web on its own—that is, it doesn’t need to read the resource
via the MCP server.

For other use cases, servers **SHOULD** prefer to use another URI scheme, or define a
custom one, even if the server will itself be downloading resource contents over the
internet.

### file://

Used to identify resources that behave like a filesystem. However, the resources do not
need to map to an actual physical filesystem.

MCP servers **MAY** identify file:// resources with an
[XDG MIME type](https://specifications.freedesktop.org/shared-mime-info-spec/0.14/ar01s02.html#id-1.3.14),
like `inode/directory`, to represent non-regular files (such as directories) that don’t
otherwise have a standard MIME type.

### git://

Git version control integration.

### Custom URI Schemes

Custom URI schemes **MUST** be in accordance with [RFC3986](https://datatracker.ietf.org/doc/html/rfc3986),
taking the above guidance in to account.

## Error Handling

Servers **SHOULD** return standard JSON-RPC errors for common failure cases:

* Resource not found: `-32002`
* Internal errors: `-32603`

Example error:

```json
{
  "jsonrpc": "2.0",
  "id": 5,
  "error": {
    "code": -32002,
    "message": "Resource not found",
    "data": {
      "uri": "file:///nonexistent.txt"
    }
  }
}
```

## Security Considerations

1. Servers **MUST** validate all resource URIs
2. Access controls **SHOULD** be implemented for sensitive resources
3. Binary data **MUST** be properly encoded
4. Resource permissions **SHOULD** be checked before operations


# Tools
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/tools



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) allows servers to expose tools that can be invoked by
language models. Tools enable models to interact with external systems, such as querying
databases, calling APIs, or performing computations. Each tool is uniquely identified by
a name and includes metadata describing its schema.

## User Interaction Model

Tools in MCP are designed to be **model-controlled**, meaning that the language model can
discover and invoke tools automatically based on its contextual understanding and the
user's prompts.

However, implementations are free to expose tools through any interface pattern that
suits their needs—the protocol itself does not mandate any specific user
interaction model.

<Warning>
  For trust & safety and security, there **SHOULD** always
  be a human in the loop with the ability to deny tool invocations.

  Applications **SHOULD**:

  * Provide UI that makes clear which tools are being exposed to the AI model
  * Insert clear visual indicators when tools are invoked
  * Present confirmation prompts to the user for operations, to ensure a human is in the
    loop
</Warning>

## Capabilities

Servers that support tools **MUST** declare the `tools` capability:

```json
{
  "capabilities": {
    "tools": {
      "listChanged": true
    }
  }
}
```

`listChanged` indicates whether the server will emit notifications when the list of
available tools changes.

## Protocol Messages

### Listing Tools

To discover available tools, clients send a `tools/list` request. This operation supports
[pagination](/specification/2025-06-18/server/utilities/pagination).

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list",
  "params": {
    "cursor": "optional-cursor-value"
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "tools": [
      {
        "name": "get_weather",
        "title": "Weather Information Provider",
        "description": "Get current weather information for a location",
        "inputSchema": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "City name or zip code"
            }
          },
          "required": ["location"]
        }
      }
    ],
    "nextCursor": "next-page-cursor"
  }
}
```

### Calling Tools

To invoke a tool, clients send a `tools/call` request:

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "get_weather",
    "arguments": {
      "location": "New York"
    }
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Current weather in New York:\nTemperature: 72°F\nConditions: Partly cloudy"
      }
    ],
    "isError": false
  }
}
```

### List Changed Notification

When the list of available tools changes, servers that declared the `listChanged`
capability **SHOULD** send a notification:

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/tools/list_changed"
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant LLM
    participant Client
    participant Server

    Note over Client,Server: Discovery
    Client->>Server: tools/list
    Server-->>Client: List of tools

    Note over Client,LLM: Tool Selection
    LLM->>Client: Select tool to use

    Note over Client,Server: Invocation
    Client->>Server: tools/call
    Server-->>Client: Tool result
    Client->>LLM: Process result

    Note over Client,Server: Updates
    Server--)Client: tools/list_changed
    Client->>Server: tools/list
    Server-->>Client: Updated tools
```

## Data Types

### Tool

A tool definition includes:

* `name`: Unique identifier for the tool
* `title`: Optional human-readable name of the tool for display purposes.
* `description`: Human-readable description of functionality
* `inputSchema`: JSON Schema defining expected parameters
* `outputSchema`: Optional JSON Schema defining expected output structure
* `annotations`: optional properties describing tool behavior

<Warning>
  For trust & safety and security, clients **MUST** consider
  tool annotations to be untrusted unless they come from trusted servers.
</Warning>

### Tool Result

Tool results may contain [**structured**](#structured-content) or **unstructured** content.

**Unstructured** content is returned in the `content` field of a result, and can contain multiple content items of different types:

#### Text Content

```json
{
  "type": "text",
  "text": "Tool result text"
}
```

#### Image Content

```json
{
  "type": "image",
  "data": "base64-encoded-data",
  "mimeType": "image/png"
}
```

#### Audio Content

```json
{
  "type": "audio",
  "data": "base64-encoded-audio-data",
  "mimeType": "audio/wav"
}
```

#### Resource Links

A tool **MAY** return links to [Resources](/specification/2025-06-18/server/resources), to provide additional context
or data. In this case, the tool will return a URI that can be subscribed to or fetched by the client:

```json
{
  "type": "resource_link",
  "uri": "file:///project/src/main.rs",
  "name": "main.rs",
  "description": "Primary application entry point",
  "mimeType": "text/x-rust"
}
```

<Info>
  Resource links returned by tools are not guaranteed to appear in the results
  of a `resources/list` request.
</Info>

#### Embedded Resources

[Resources](/specification/2025-06-18/server/resources) **MAY** be embedded to provide additional context
or data using a suitable [URI scheme](./resources#common-uri-schemes). Servers that use embedded resources **SHOULD** implement the `resources` capability:

```json
{
  "type": "resource",
  "resource": {
    "uri": "file:///project/src/main.rs",
    "title": "Project Rust Main File",
    "mimeType": "text/x-rust",
    "text": "fn main() {\n    println!(\"Hello world!\");\n}"
  }
}
```

#### Structured Content

**Structured** content is returned as a JSON object in the `structuredContent` field of a result.

For backwards compatibility, a tool that returns structured content SHOULD also return functionally equivalent unstructured content.
(For example, serialized JSON can be returned in a `TextContent` block.)

#### Output Schema

Tools may also provide an output schema for validation of structured results.
If an output schema is provided:

* Servers **MUST** provide structured results that conform to this schema.
* Clients **SHOULD** validate structured results against this schema.

Example tool with output schema:

```json
{
  "name": "get_weather_data",
  "title": "Weather Data Retriever",
  "description": "Get current weather data for a location",
  "inputSchema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City name or zip code"
      }
    },
    "required": ["location"]
  },
  "outputSchema": {
    "type": "object",
    "properties": {
      "temperature": {
        "type": "number",
        "description": "Temperature in celsius"
      },
      "conditions": {
        "type": "string",
        "description": "Weather conditions description"
      },
      "humidity": {
        "type": "number",
        "description": "Humidity percentage"
      }
    },
    "required": ["temperature", "conditions", "humidity"]
  }
}
```

Example valid response for this tool:

```json
{
  "jsonrpc": "2.0",
  "id": 5,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"temperature\": 22.5, \"conditions\": \"Partly cloudy\", \"humidity\": 65}"
      }
    ],
    "structuredContent": {
      "temperature": 22.5,
      "conditions": "Partly cloudy",
      "humidity": 65
    }
  }
}
```

Providing an output schema helps clients and LLMs understand and properly handle structured tool outputs by:

* Enabling strict schema validation of responses
* Providing type information for better integration with programming languages
* Guiding clients and LLMs to properly parse and utilize the returned data
* Supporting better documentation and developer experience

## Error Handling

Tools use two error reporting mechanisms:

1. **Protocol Errors**: Standard JSON-RPC errors for issues like:

   * Unknown tools
   * Invalid arguments
   * Server errors

2. **Tool Execution Errors**: Reported in tool results with `isError: true`:
   * API failures
   * Invalid input data
   * Business logic errors

Example protocol error:

```json
{
  "jsonrpc": "2.0",
  "id": 3,
  "error": {
    "code": -32602,
    "message": "Unknown tool: invalid_tool_name"
  }
}
```

Example tool execution error:

```json
{
  "jsonrpc": "2.0",
  "id": 4,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Failed to fetch weather data: API rate limit exceeded"
      }
    ],
    "isError": true
  }
}
```

## Security Considerations

1. Servers **MUST**:

   * Validate all tool inputs
   * Implement proper access controls
   * Rate limit tool invocations
   * Sanitize tool outputs

2. Clients **SHOULD**:
   * Prompt for user confirmation on sensitive operations
   * Show tool inputs to the user before calling the server, to avoid malicious or
     accidental data exfiltration
   * Validate tool results before passing to LLM
   * Implement timeouts for tool calls
   * Log tool usage for audit purposes


# Completion
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/utilities/completion



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) provides a standardized way for servers to offer
argument autocompletion suggestions for prompts and resource URIs. This enables rich,
IDE-like experiences where users receive contextual suggestions while entering argument
values.

## User Interaction Model

Completion in MCP is designed to support interactive user experiences similar to IDE code
completion.

For example, applications may show completion suggestions in a dropdown or popup menu as
users type, with the ability to filter and select from available options.

However, implementations are free to expose completion through any interface pattern that
suits their needs—the protocol itself does not mandate any specific user
interaction model.

## Capabilities

Servers that support completions **MUST** declare the `completions` capability:

```json
{
  "capabilities": {
    "completions": {}
  }
}
```

## Protocol Messages

### Requesting Completions

To get completion suggestions, clients send a `completion/complete` request specifying
what is being completed through a reference type:

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "completion/complete",
  "params": {
    "ref": {
      "type": "ref/prompt",
      "name": "code_review"
    },
    "argument": {
      "name": "language",
      "value": "py"
    }
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "completion": {
      "values": ["python", "pytorch", "pyside"],
      "total": 10,
      "hasMore": true
    }
  }
}
```

For prompts or URI templates with multiple arguments, clients should include previous completions in the `context.arguments` object to provide context for subsequent requests.

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "completion/complete",
  "params": {
    "ref": {
      "type": "ref/prompt",
      "name": "code_review"
    },
    "argument": {
      "name": "framework",
      "value": "fla"
    },
    "context": {
      "arguments": {
        "language": "python"
      }
    }
  }
}
```

**Response:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "completion": {
      "values": ["flask"],
      "total": 1,
      "hasMore": false
    }
  }
}
```

### Reference Types

The protocol supports two types of completion references:

| Type           | Description                 | Example                                             |
| -------------- | --------------------------- | --------------------------------------------------- |
| `ref/prompt`   | References a prompt by name | `{"type": "ref/prompt", "name": "code_review"}`     |
| `ref/resource` | References a resource URI   | `{"type": "ref/resource", "uri": "file:///{path}"}` |

### Completion Results

Servers return an array of completion values ranked by relevance, with:

* Maximum 100 items per response
* Optional total number of available matches
* Boolean indicating if additional results exist

## Message Flow

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Note over Client: User types argument
    Client->>Server: completion/complete
    Server-->>Client: Completion suggestions

    Note over Client: User continues typing
    Client->>Server: completion/complete
    Server-->>Client: Refined suggestions
```

## Data Types

### CompleteRequest

* `ref`: A `PromptReference` or `ResourceReference`
* `argument`: Object containing:
  * `name`: Argument name
  * `value`: Current value
* `context`: Object containing:
  * `arguments`: A mapping of already-resolved argument names to their values.

### CompleteResult

* `completion`: Object containing:
  * `values`: Array of suggestions (max 100)
  * `total`: Optional total matches
  * `hasMore`: Additional results flag

## Error Handling

Servers **SHOULD** return standard JSON-RPC errors for common failure cases:

* Method not found: `-32601` (Capability not supported)
* Invalid prompt name: `-32602` (Invalid params)
* Missing required arguments: `-32602` (Invalid params)
* Internal errors: `-32603` (Internal error)

## Implementation Considerations

1. Servers **SHOULD**:

   * Return suggestions sorted by relevance
   * Implement fuzzy matching where appropriate
   * Rate limit completion requests
   * Validate all inputs

2. Clients **SHOULD**:
   * Debounce rapid completion requests
   * Cache completion results where appropriate
   * Handle missing or partial results gracefully

## Security

Implementations **MUST**:

* Validate all completion inputs
* Implement appropriate rate limiting
* Control access to sensitive suggestions
* Prevent completion-based information disclosure


# Logging
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/utilities/logging



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) provides a standardized way for servers to send
structured log messages to clients. Clients can control logging verbosity by setting
minimum log levels, with servers sending notifications containing severity levels,
optional logger names, and arbitrary JSON-serializable data.

## User Interaction Model

Implementations are free to expose logging through any interface pattern that suits their
needs—the protocol itself does not mandate any specific user interaction model.

## Capabilities

Servers that emit log message notifications **MUST** declare the `logging` capability:

```json
{
  "capabilities": {
    "logging": {}
  }
}
```

## Log Levels

The protocol follows the standard syslog severity levels specified in
[RFC 5424](https://datatracker.ietf.org/doc/html/rfc5424#section-6.2.1):

| Level     | Description                      | Example Use Case           |
| --------- | -------------------------------- | -------------------------- |
| debug     | Detailed debugging information   | Function entry/exit points |
| info      | General informational messages   | Operation progress updates |
| notice    | Normal but significant events    | Configuration changes      |
| warning   | Warning conditions               | Deprecated feature usage   |
| error     | Error conditions                 | Operation failures         |
| critical  | Critical conditions              | System component failures  |
| alert     | Action must be taken immediately | Data corruption detected   |
| emergency | System is unusable               | Complete system failure    |

## Protocol Messages

### Setting Log Level

To configure the minimum log level, clients **MAY** send a `logging/setLevel` request:

**Request:**

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "logging/setLevel",
  "params": {
    "level": "info"
  }
}
```

### Log Message Notifications

Servers send log messages using `notifications/message` notifications:

```json
{
  "jsonrpc": "2.0",
  "method": "notifications/message",
  "params": {
    "level": "error",
    "logger": "database",
    "data": {
      "error": "Connection failed",
      "details": {
        "host": "localhost",
        "port": 5432
      }
    }
  }
}
```

## Message Flow

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Note over Client,Server: Configure Logging
    Client->>Server: logging/setLevel (info)
    Server-->>Client: Empty Result

    Note over Client,Server: Server Activity
    Server--)Client: notifications/message (info)
    Server--)Client: notifications/message (warning)
    Server--)Client: notifications/message (error)

    Note over Client,Server: Level Change
    Client->>Server: logging/setLevel (error)
    Server-->>Client: Empty Result
    Note over Server: Only sends error level<br/>and above
```

## Error Handling

Servers **SHOULD** return standard JSON-RPC errors for common failure cases:

* Invalid log level: `-32602` (Invalid params)
* Configuration errors: `-32603` (Internal error)

## Implementation Considerations

1. Servers **SHOULD**:

   * Rate limit log messages
   * Include relevant context in data field
   * Use consistent logger names
   * Remove sensitive information

2. Clients **MAY**:
   * Present log messages in the UI
   * Implement log filtering/search
   * Display severity visually
   * Persist log messages

## Security

1. Log messages **MUST NOT** contain:

   * Credentials or secrets
   * Personal identifying information
   * Internal system details that could aid attacks

2. Implementations **SHOULD**:
   * Rate limit messages
   * Validate all data fields
   * Control log access
   * Monitor for sensitive content


# Pagination
Source: https://modelcontextprotocol.io/specification/2025-06-18/server/utilities/pagination



<div id="enable-section-numbers" />

<Info>**Protocol Revision**: 2025-06-18</Info>

The Model Context Protocol (MCP) supports paginating list operations that may return
large result sets. Pagination allows servers to yield results in smaller chunks rather
than all at once.

Pagination is especially important when connecting to external services over the
internet, but also useful for local integrations to avoid performance issues with large
data sets.

## Pagination Model

Pagination in MCP uses an opaque cursor-based approach, instead of numbered pages.

* The **cursor** is an opaque string token, representing a position in the result set
* **Page size** is determined by the server, and clients **MUST NOT** assume a fixed page
  size

## Response Format

Pagination starts when the server sends a **response** that includes:

* The current page of results
* An optional `nextCursor` field if more results exist

```json
{
  "jsonrpc": "2.0",
  "id": "123",
  "result": {
    "resources": [...],
    "nextCursor": "eyJwYWdlIjogM30="
  }
}
```

## Request Format

After receiving a cursor, the client can *continue* paginating by issuing a request
including that cursor:

```json
{
  "jsonrpc": "2.0",
  "method": "resources/list",
  "params": {
    "cursor": "eyJwYWdlIjogMn0="
  }
}
```

## Pagination Flow

```mermaid
sequenceDiagram
    participant Client
    participant Server

    Client->>Server: List Request (no cursor)
    loop Pagination Loop
      Server-->>Client: Page of results + nextCursor
      Client->>Server: List Request (with cursor)
    end
```

## Operations Supporting Pagination

The following MCP operations support pagination:

* `resources/list` - List available resources
* `resources/templates/list` - List resource templates
* `prompts/list` - List available prompts
* `tools/list` - List available tools

## Implementation Guidelines

1. Servers **SHOULD**:

   * Provide stable cursors
   * Handle invalid cursors gracefully

2. Clients **SHOULD**:

   * Treat a missing `nextCursor` as the end of results
   * Support both paginated and non-paginated flows

3. Clients **MUST** treat cursors as opaque tokens:
   * Don't make assumptions about cursor format
   * Don't attempt to parse or modify cursors
   * Don't persist cursors across sessions

## Error Handling

Invalid cursors **SHOULD** result in an error with code -32602 (Invalid params).


# Versioning
Source: https://modelcontextprotocol.io/specification/versioning



The Model Context Protocol uses string-based version identifiers following the format
`YYYY-MM-DD`, to indicate the last date backwards incompatible changes were made.

<Info>
  The protocol version will *not* be incremented when the
  protocol is updated, as long as the changes maintain backwards compatibility. This allows
  for incremental improvements while preserving interoperability.
</Info>

## Revisions

Revisions may be marked as:

* **Draft**: in-progress specifications, not yet ready for consumption.
* **Current**: the current protocol version, which is ready for use and may continue to
  receive backwards compatible changes.
* **Final**: past, complete specifications that will not be changed.

The **current** protocol version is [**2025-06-18**](/specification/2025-06-18/).

## Negotiation

Version negotiation happens during
[initialization](/specification/2025-06-18/basic/lifecycle#initialization). Clients and
servers **MAY** support multiple protocol versions simultaneously, but they **MUST**
agree on a single version to use for the session.

The protocol provides appropriate error handling if version negotiation fails, allowing
clients to gracefully terminate connections when they cannot find a version compatible
with the server.


# Building MCP with LLMs
Source: https://modelcontextprotocol.io/tutorials/building-mcp-with-llms

Speed up your MCP development using LLMs such as Claude!

This guide will help you use LLMs to help you build custom Model Context Protocol (MCP) servers and clients. We'll be focusing on Claude for this tutorial, but you can do this with any frontier LLM.

## Preparing the documentation

Before starting, gather the necessary documentation to help Claude understand MCP:

1. Visit [https://modelcontextprotocol.io/llms-full.txt](https://modelcontextprotocol.io/llms-full.txt) and copy the full documentation text
2. Navigate to either the [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk) or [Python SDK repository](https://github.com/modelcontextprotocol/python-sdk)
3. Copy the README files and other relevant documentation
4. Paste these documents into your conversation with Claude

## Describing your server

Once you've provided the documentation, clearly describe to Claude what kind of server you want to build. Be specific about:

* What resources your server will expose
* What tools it will provide
* Any prompts it should offer
* What external systems it needs to interact with

For example:

```
Build an MCP server that:
- Connects to my company's PostgreSQL database
- Exposes table schemas as resources
- Provides tools for running read-only SQL queries
- Includes prompts for common data analysis tasks
```

## Working with Claude

When working with Claude on MCP servers:

1. Start with the core functionality first, then iterate to add more features
2. Ask Claude to explain any parts of the code you don't understand
3. Request modifications or improvements as needed
4. Have Claude help you test the server and handle edge cases

Claude can help implement all the key MCP features:

* Resource management and exposure
* Tool definitions and implementations
* Prompt templates and handlers
* Error handling and logging
* Connection and transport setup

## Best practices

When building MCP servers with Claude:

* Break down complex servers into smaller pieces
* Test each component thoroughly before moving on
* Keep security in mind - validate inputs and limit access appropriately
* Document your code well for future maintenance
* Follow MCP protocol specifications carefully

## Next steps

After Claude helps you build your server:

1. Review the generated code carefully
2. Test the server with the MCP Inspector tool
3. Connect it to Claude.app or other MCP clients
4. Iterate based on real usage and feedback

Remember that Claude can help you modify and improve your server as requirements change over time.

Need more guidance? Just ask Claude specific questions about implementing MCP features or troubleshooting issues that arise.