Measuring what matters: How offline evaluation of GitHub MCP Server works

MCP (Model Context Protocol) is a standardized method enabling AI models, particularly large language models (LLMs), to interact with APIs and data by utilizing a universal interface. This protocol facilitates the integration of AI models with tools provided by MCP servers, such as the GitHub MCP Server, which underpins many GitHub Copilot workflows. The process involves the MCP server publishing available tools and their parameters, while an agent connects to these servers to relay tool information and user requests to the LLM, which then determines the necessary tools and arguments to fulfill the request. Offline evaluation plays a crucial role in ensuring the efficacy and quality of MCP by testing tool prompts across different models to identify and rectify regressions before reaching users. The evaluation pipeline is divided into fulfillment, evaluation, and summarization stages, focusing on tool selection and argument correctness, with metrics like accuracy, precision, recall, and F1-score used to assess performance. Challenges such as limited benchmark volume and the need for multi-tool flow evaluations are acknowledged, with plans to expand benchmark coverage and refine tool descriptions to enhance clarity and reliability.