Article Summary
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.