
greynewell/mcpbr
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Benchmark your MCP server.
개요
Benchmark your MCP server against real GitHub issues. One command, hard numbers. Stop guessing if your MCP server actually helps. Get hard numbers comparing tool-assisted vs. baseline agent performance on real GitHub issues. If this is useful, please star our tools — it helps us grow: Real metrics showing whether your MCP server improves agent performance on SWE-bench tasks. No vibes, just data. MCP servers promise to make LLMs better at coding tasks. But how do you prove it? mcpbr runs controlled experiments: same model, same tasks, same environment - the only variable is your MCP server. You get: - against a baseline agent - from SWE-bench (not toy examples) - via Docker containers with pinned dependencies - SWE-bench Verified Is Broken: 5 Things I Found in the Source Code - SWE-bench Tests Run 6x Faster on ARM64 with Native Containers
README
원문 README의 일부입니다. 원문 보기.
mcpbr
# One-liner install (installs + runs quick test)
curl -sSL https://raw.githubusercontent.com/greynewell/mcpbr/main/install.sh | bash
# Or install and run manually
pip install mcpbr && mcpbr run -n 1
Benchmark your MCP server against real GitHub issues. One command, hard numbers.
Model Context Protocol Benchmark Runner
Stop guessing if your MCP server actually helps. Get hard numbers comparing tool-assisted vs. baseline agent performance on real GitHub issues.
⭐ Star the Supermodel Ecosystem
If this is useful, please star our tools — it helps us grow:
설치
npx -y @modelcontextprotocol/server-filesystem {workdir}