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Model Context Protocol (MCP) Python Implementation

This repository contains a Python implementation of the Model Context Protocol (MCP), demonstrating how to create both a server and client application that communicate using the MCP standard.

Overview

The MCP implementation consists of two main components:

  1. MCP Server (server.py) - A server that exposes:

    • Tools: Functions that can be called remotely
    • Resources: Data that can be retrieved
    • Prompts: Templates for generating prompts for language models

  2. MCP Client (client.py) - A client application that connects to the server and uses its features

Features

This implementation demonstrates several key MCP features:

Tools

  • completion - Generates text completions from AI models (simulated)
  • add - Simple calculator that adds two numbers

Resources

  • models:// - Returns information about available AI models
  • greeting://{name} - Returns a personalized greeting for a given name

Prompts

  • review_code - Generates a prompt for reviewing code

Installation

To use this MCP implementation, install the required packages:

pip install mcp-server mcp-client

Running the Server and Client

Starting the Server

Run the server in one terminal window:

python server.py

The server can also be run in development mode using the MCP CLI:

mcp dev server.py

Or installed in Claude Desktop (if available):

mcp install server.py

Running the Client

Run the client in another terminal window:

python client.py

This will connect to the server and demonstrate all available features.

Client Usage

The client (client.py) demonstrates all the MCP capabilities:

python client.py

This will connect to the server and exercise all features including tools, resources, and prompts. The output will show:

  1. Calculator tool result (5 + 7 = 12)
  2. Completion tool response to "What is the meaning of life?"
  3. List of available AI models
  4. Personalized greeting for "MCP Explorer"
  5. Code review prompt template

Implementation Details

The server is implemented using the FastMCP API, which provides high-level abstractions for defining MCP services. Here's a simplified example of how tools are defined:

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers together
    
    Args:
        a: First number
        b: Second number
    
    Returns:
        The sum of the two numbers
    """
    logger.info(f"Adding {a} and {b}")
    return a + b

The client uses the MCP client library to connect to and call the server:

async with stdio_client(server_params) as (reader, writer):
    async with ClientSession(reader, writer) as session:
        await session.initialize()
        result = await session.call_tool("add", arguments={"a": 5, "b": 7})

Learn More

For more information about MCP, visit: https://modelcontextprotocol.io/