CodeCanvas MCP

Understand an unfamiliar Python system before spending thousands of tokens reading it file by file.

CodeCanvas is a local static-analysis Model Context Protocol server for Python. It turns project-wide call paths and control flow into compact, citation-ready answers about branches, callers, callees, side effects, and change impact.

The benchmark now spans pinned revisions of Google ADK, LangGraph, and FastAPI. On an Apple M4 Pro, measured cold analysis ranged from 4.38s to 61.62s, and median warm find_symbols latency ranged from 48.264ms to 293.778ms across those repositories. In a controlled 54-session agent suite, both conditions kept the same built-in code-search tools; the treatment added only logic_flow. That single addition used a median 22.95% fewer total tokens across three paired repetitions, demonstrating meaningful incremental value on top of ordinary code exploration. Uncached tokens increased by 0.78%, and the answers are not yet blind-graded. See the methodology, full tables, and limitations.

Use it to answer questions such as:

• Who calls this function, directly or transitively?
• What can this function reach, and where do side effects happen?
• Under which guards can this return or exception occur?
• Does this source really reach that target in the requested mode?
• Which API routes, scripts, or public exports are affected by a diff?

CodeCanvas is Python-only and requires Python 3.10 or newer.

See the difference

Ask one question:

Use logic_flow on UserService.update_user. Show its branches, outcomes,
downstream effects, and evidence quality.

Excerpt from the actual response on the included FastAPI sample:

{
  "function": "app.services.user_service.UserService.update_user",
  "source": "app/services/user_service.py:13",
  "flow": [
    "15  user = await self.user_repo.find_by_id(...)",
    "16  if user is None:",
    "17      → return None",
    "18  → return await self.user_repo.update(user_id, user)"
  ],
  "outcomes": [
    {"at": 17, "detail": "None", "guards": ["user is None"]},
    {"at": 18, "detail": "await self.user_repo.update(user_id, user)", "guards": []}
  ],
  "downstream": [
    {
      "function": "app.repositories.user_repo.UserRepository.find_by_id",
      "location": "app/repositories/user_repo.py:13",
      "effects": ["db"]
    },
    {
      "function": "app.repositories.user_repo.UserRepository.update",
      "location": "app/repositories/user_repo.py:18",
      "effects": ["db"]
    }
  ],
  "evidence_grade": "inferred",
  "safe_to_summarize": false,
  "response_guidance": "Do not turn inferred call edges into unconditional claims."
}

That single response exposes the early return, success path, downstream database work, exact source locations, and how cautiously the agent may summarize the result.

Quick start

Install uv if uvx is not already available. The repository includes one shared plugin package with native manifests for both Claude Code and Codex. Install it from the CodeCanvas marketplace:

# Claude Code
claude plugin marketplace add donggyun112/codecanvas
claude plugin install codecanvas@codecanvas

# Codex
codex plugin marketplace add donggyun112/codecanvas
codex plugin add codecanvas@codecanvas

Both plugins start uvx codecanvas-mcp and expose the complete tool catalog. See the plugin package for local-checkout testing

... View full README on GitHub