mcp-doctor

A static analysis CLI that audits MCP (Model Context Protocol) server implementations for the things that actually break an agent calling them: missing tool descriptions, undocumented parameters, no error handling, no README coverage.

The MCP ecosystem is growing faster than the conventions around building a good server have settled. Most servers are hand-written in an afternoon and never checked against anything. mcp-doctor is a linter for that gap — point it at a repo, get a score and a concrete list of what to fix.

$ mcp-doctor examples/bad_server
mcp-doctor report
Score: 13%  Grade: F  (2 tool(s) found)

  [FAIL] do_thing (server.py:9)
      ERROR  Tool has no description. An agent cannot decide when to call this.
      WARNING  2/2 parameters have no type annotation.
      WARNING  Parameters aren't documented in an Args: section — the model only sees names, not intent.
      WARNING  No try/except — an exception here will raise a raw traceback back through the MCP transport.
  [FAIL] run (server.py:15)
      ERROR  Tool has no description. An agent cannot decide when to call this.
      WARNING  1/1 parameters have no type annotation.
      WARNING  Parameters aren't documented in an Args: section — the model only sees names, not intent.
      ERROR  Bare 'except:' swallows all errors including cancellation — catch specific exceptions.

Repo-level
  ERROR  No README found.
  WARNING  No LICENSE file — undermines adoption.
  WARNING  No test files found.
  WARNING  No pyproject.toml/requirements.txt/setup.py — dependencies aren't pinned.

$ mcp-doctor examples/good_server
mcp-doctor report
Score: 100%  Grade: A  (1 tool(s) found)

  [OK] get_forecast (server.py:9)

Install

pip install mcp-server-lint

(The PyPI project is named mcp-server-lint — mcp-doctor and every close variant of it were already taken or blocked by PyPI's anti-typosquat check — but the installed command is still mcp-doctor.)

Or install straight from the repo:

pip install git+https://github.com/vishalhabib99/mcp-doctor.git

or clone it and install locally:

git clone https://github.com/vishalhabib99/mcp-doctor.git
cd mcp-doctor
pip install -e .

Usage

mcp-doctor .                      # audit the current directory
mcp-doctor path/to/server         # audit a specific path
mcp-doctor . --json               # machine-readable output
mcp-doctor . --fail-under 80      # exit 1 if score drops below 80% — wire into CI

GitHub Action

Gate PRs on server quality without installing anything yourself:

- uses: vishalhabib99/mcp-doctor@v1
  with:
    path: .              # default: repo root
    fail-under: 70        # default: 0 (report only, don't fail the build)
    comment: true          # default: true — posts/updates a PR comment with the report

The report also gets written to the job summary either way. @v1 tracks the latest v1.x release; pin an exact tag or commit SHA instead if you need stricter reproducibility.

What it checks

Audits both Python and TypeScript/JavaScript servers in the same repo. Python detects the FastMCP @mcp.tool() decorator style and the low-level SDK's Tool(name=..., description=..., inputSchema=...) style; TS/JS detects the official SDK's server.registerTool(name, config, handler) and server.tool(name, description, schema, handler) styles, including the common pattern where the config object or Zod schema is a same-file const reference rather than inline. The same checks apply either way — a description, per-parameter docs (Args:/Field(description=...) in Python, .describe(...) on each Zod field in TS), and a try/except (or try/catch).

Per tool:

Repo-level:

• README exists, and mentions every tool you export
• LICENSE exists
• Tests exist
• Dependencies are declared (pyproject.toml / requirements.txt / setup.py / package.json)
• No hardcoded-looking API keys/secrets/tokens in source
• Tool names conform to the spec's Tool Names guidance (1–128 chars, A-Z a-z 0-9 _ - . only, unique within the server)

Real-world spot check

Run against three servers from the official modelcontextprotocol/servers repo:

• src/fetch — 100% / A. Clean.
• src/git, src/time — flagged as parse errors, not false passes. Both use Python match statements (3.10+ syntax); mcp-doctor's AST parser follows the grammar of whatever Python interpreter runs it, so under Python 3.9 those files can't be parsed. Rather than silently skip them and report a misleadingly clean score, mcp-doctor surfaces this as an explicit error: "N file(s) could not be parsed and were skipped." Run it under Python ≥3.10 to analyze those files correctly.

Later spot-checked against 4 more real, in-the-wild servers (awslabs' aws-documentation-mcp-server, mcp-google-ads, sv-excel-agent, and Home Assistant's ha-mcp, an 88-tool server). That run caught two real precision bugs: the secret scanner was flagging test fixtures and identifier-style constant names (SERVICE_GET_CALLER_TOKEN = "get_caller_token") as hardcoded credentials, and the param-docs check didn't recognize Annotated[T, Field(description=...)] — a completely valid, schema-level way to document a parameter — as documentation at all, since it only looked for a docstring Args: section. Both fixed.

A maintainer on ha-mcp reviewed the resulting report in detail and pushed back further, correctly: the param-docs check still missed descriptions reached through a shared, cross-file type alias (Annotated[..., Field(description=...)] assigned to a name and imported elsewhere) and prose under non-Args: headings (e.g. **Parameters:**, including bulleted - param: ... lines), and — more importantly — the error-handling check's own message was wrong. It claimed a missing try/except lets a raw traceback leak through the MCP transport; FastMCP's call_tool dispatcher actually wraps every call and converts any exception into a structured error regardless, which the pushback prompted me to verify directly against FastMCP's source. Both the alias/heading gaps and the error-handling message are now fixed — see homeassistant-ai/ha-mcp#2324 for the full exchange.

The maintainer offered to leave a follow-up issue open if it were grounded in the actual spec and FastMCP's own guidelines rather than another pass of the same heuristics. Read the current spec's Tools page end to end looking for exactly that: one concrete, checkable gap emerged — the normative Tool Names section (length, character set, uniqueness), which mcp-doctor didn't check at all — now added. Checked it against ha-mcp's real 88 tool names before claiming anything: all of them already comply, so this doesn't reopen anything there — it's a real gap closed for the next server that isn't as careful, not a finding to hand back.

Known limitations

• AST-based, single-pass. Tools constructed dynamically in a loop, or schemas built from something other than a dict literal or a pydantic model_json_schema() call, won't be fully introspected — you'll get the tool detected but a blind spot on its parameter-level checks rather than a false failure. A dynamic tool name (not a string literal, e.g. built in a loop) means the tool is skipped entirely rather than misattributed.
• Parses with the running interpreter's grammar (Python side). See the spot check above — run under a Python version that matches or exceeds the syntax used in the server you're auditing.
• Doesn't follow delegation. If a tool function immediately hands off to a helper that has its own try/except (or try/catch), the error-handling check only looks at the decorated function's own body and reports a false positive — it has no call-graph analysis.
• TS/JS const resolution is same-file only. Unlike the Python side's cross-file Field type-alias resolution, a TS config object or Zod schema referenced via an import from another file won't be resolved — only same-file const references.

Roadmap

• TypeScript/JS server support (the official SDK's dominant language) — registerTool/tool styles, same-file const resolution
• Publish to PyPI
• --fix for the mechanical stuff (stub Args: sections, wrap in try/except)
• GitHub Action for one-line CI integration

Contributing

Issues and PRs welcome. The test suite (pytest) covers the analyzer directly and the CLI end-to-end against the fixtures in examples/ — add a fixture case for anything you fix.

License

MIT — see LICENSE.