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greynewell/mcpbr

Developer tools

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11 stars 2 forks 품질 61 트렌드 8MIT

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:

GitHub에서 전체 README 보기

설치

npx -y @modelcontextprotocol/server-filesystem {workdir}
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