Mcpbr: Stop guessing and evaluate your MCP server against standard benchmarks (github.com)

The newly announced tool, MCP Benchmark Runner (mcpbr), allows users to evaluate Model Context Protocol (MCP) servers against Software Engineering benchmarks (SWE-bench) tasks, marking a significant advancement for developers working with large language models (LLMs) in coding environments. By providing hard metrics on agent performance through controlled, reproducible experiments, mcpbr removes the guesswork from assessing whether MCP servers indeed enhance coding tasks, which is crucial as the demand for reliable AI coding assistance grows. The mcpbr tool runs parallel evaluations involving an MCP agent with access to tools versus a baseline agent without tools, using real GitHub issues rather than simplistic test cases. The configuration is straightforward, enabling users to initiate evaluations via simple command-line instructions post-installation. This includes detailed metrics such as resolution rates and success rates for each task, all stored in JSON format for further analysis. With support for models like Claude Opus and Sonnet, and the requirement for Docker and specific dependencies, mcpbr is positioned to facilitate significant improvements in AI-driven coding by systematically proving the effectiveness of MCP integrations.