文章摘要
GitHub has outlined its comprehensive offline evaluation strategy for the Model Context Protocol (MCP) Server, which is central to delivering relevant context to generative AI tools like Copilot Chat.
- The MCP Server's primary function is to intelligently retrieve and provide contextual information from a user's workspace to large language models.
- Evaluation relies on creating high-quality datasets of good context examples, alongside metrics like precision and recall to measure retrieval accuracy.
- Human evaluators play a critical role, assessing the usefulness, accuracy, and completeness of the context retrieved by the server for various queries.
- This continuous offline evaluation process is vital for iterating and improving the MCP Server, ultimately enhancing the quality and relevance of AI assistant responses.