Debug Companion MCP

Debug Companion MCP

Enables AI coding agents to debug Python projects by running pytest, extracting failure locations, displaying code context around failures, and optionally requesting fix suggestions from Gemini.

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README

Debug Companion MCP (Pytest Debugging Agent)

A local MCP server that helps an AI coding agent debug Python projects by:

  • Running pytest safely via subprocess
  • Extracting failure locations (file:line)
  • Opening code context around the failure
  • (Optional) Asking Gemini for a fix suggestion

Requirements

  • Python 3.12+
  • uv

Install

uv sync

Run the MCP server

uv run python server.py

Available tools

  • ping — health check
  • run_pytest(target, max_output_lines, timeout_seconds) — run pytest safely
  • extract_failures(pytest_output, limit, base_dir) — parse file.py:line: from pytest output
  • open_context(path, line, radius, base_dir) — return a code window around a line
  • debug_project(target, ...) — orchestrates: run_pytest → extract_failures → open_context → (optional) Gemini analysis

Recommended demo flow

  1. Run debug_project on demo_project (intentionally contains a failing test).
  2. Observe: extracted file:line + code context window.
  3. Apply the suggested fix (or a one-line fix manually).
  4. Re-run → tests pass.

Environment variables

  • GEMINI_API_KEY — enable Gemini analysis (optional)
  • MCP_ALLOWED_ROOTS — allow access to absolute paths outside the server root (optional)

CI

GitHub Actions runs server tests on each push/PR:

  • workflow: .github/workflows/tests.yml

Notes

  • demo_project is intended for demonstration and may contain a deliberate failing test.
  • Server tests live under tests/.

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