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Model Context Protocol (MCP)

The simple way to connect AI tools to data sources like GitHub, Google Drive, and Slack

It’s a protocol to allow Claude (or other LLMs) to interface with external tools (databases, web servers, file systems etc)

Watch: Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic

Introducing the Model Context Protocol

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The Model Context Protocol (MCP) is an open standard developed by Anthropic to facilitate secure and standardized connections between AI assistants, such as Claude, and various data sources, including content repositories, business tools, and development environments. This protocol enables AI systems to access and interact with both local and remote resources, enhancing their ability to provide relevant and context-aware responses.

Key Features of MCP:

  • Universal Connectivity: MCP provides a standardized method for AI applications to connect with diverse data sources without the need for custom integrations for each dataset.

  • Client-Server Architecture: The protocol operates on a client-server model where:

    • MCP Hosts: Applications like Claude Desktop that initiate connections.
    • MCP Servers: Programs that expose specific capabilities and resources through the protocol.

  • Secure Data Access: MCP ensures that AI systems can access necessary data securely, maintaining control over what information is shared and how it's utilized.

Implementing MCP with Claude Desktop:

To leverage MCP with Claude Desktop, users can configure the application to connect to various MCP servers, enabling interactions with different data sources. For instance, integrating Claude Desktop with a local SQLite database involves setting up an MCP server that facilitates communication between Claude and the database.

Benefits of MCP:

  • Streamlined Integrations: Developers can build against a standard protocol, reducing the need for maintaining separate connectors for each data source.

  • Enhanced AI Capabilities: By accessing a broader range of data, AI assistants can provide more accurate and contextually relevant responses.

  • Scalability: MCP's standardized approach facilitates the development of AI systems that can maintain context as they interact with various tools and datasets, promoting a more sustainable architecture.