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Reflections on building with Model Context Protocol (MCP)

Dec 15, 2024 - outlore.dev
The article discusses the Model Context Protocol (MCP), a new open standard by Anthropic designed to connect large language models (LLMs) with external tools and data. MCP allows AI applications like Claude Desktop to interface with local and remote servers, accessing resources such as databases and APIs. While MCP offers advantages over existing standards like OpenAPI, particularly in standardizing interactions for LLMs, it has limitations. Claude Desktop, a primary MCP client, does not fully implement the MCP specification, lacking features like SSE transport and dynamic resource management. The article also highlights issues with client-side timeouts and the need for manual configuration, suggesting that MCP could benefit from an official registry and improved client-server interaction capabilities.

The author evaluates the developer experience (DX) of MCP, noting that while the Python SDK has critical bugs, the TypeScript SDK is more robust. The documentation is considered decent but could be improved by integrating the specification with the main website. The article envisions future enhancements for MCP, such as asynchronous task scheduling, automatic prompt selection, and sessionized client-server interactions. These improvements could enhance the usability and integration of AI applications, enabling more seamless and collaborative use of tools across different platforms.

Key takeaways:

  • The Model Context Protocol (MCP) is designed to connect LLMs with external tools and data, offering more tailored interactions than existing standards like OpenAPI.
  • Claude Desktop, a key MCP client, does not fully implement the MCP specification, lacking features like SSE transport and dynamic resource management.
  • The MCP SDKs have mixed reviews, with the TypeScript SDK being praised for its API and type definitions, while the Python SDK has critical bugs affecting server stability.
  • There is potential for MCP to enhance user experiences by integrating server configuration, supporting asynchronous tasks, and enabling collaborative AI client interactions.
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