Reflections on building with Model Context Protocol

The Model Context Protocol (MCP), developed by Anthropic, is an open standard aimed at enhancing the interaction between large language models (LLMs) and external tools and data sources. It allows applications like Claude Desktop to connect with various servers, enabling access to resources such as databases and APIs. While MCP offers advantages over OpenAPI, particularly in standardizing LLM interactions, its implementation in Claude Desktop is not comprehensive. Key limitations include the lack of support for server-sent events (SSE), manual selection of prompts and resources, and strict timeouts on tool calls, which hinder long-running tasks. The MCP Inspector tool is available for testing MCP servers, but it also has its own limitations, particularly with complex input schemas. The development experience (DX) is mixed; the Python SDK has critical bugs, while the TypeScript SDK is praised for its usability. Documentation is adequate but could be improved by integrating the specification more effectively. Although MCP opens new possibilities for AI applications, it lacks a streamlined configuration process for servers, which complicates user experience. Future enhancements could include an official registry for servers, asynchronous work scheduling, automatic prompt selection, and session management for collaborative tasks among multiple clients.

- MCP enhances LLM interaction with external tools and data sources.

- Claude Desktop's implementation of MCP has notable limitations.

- The TypeScript SDK is well-received, while the Python SDK has critical issues.

- Documentation could be improved for better user guidance.

- Future developments could streamline server configuration and enhance collaborative capabilities.