Docvet MCP Server

Docvet MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. Docstring quality vetting for Python -- enrichment, freshness, coverage, and presence checks.

What you get

ruff checks how your docstrings look. interrogate checks if they exist (but is unmaintained). docvet checks if they're right — and now covers presence too. Existing tools cover style; docvet delivers the layers they miss:

Setting it up

The server ships on PyPI as docvet, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

What the assistant can call

Once Docvet is connected, these are the calls the assistant has available:

• repo — https://github.com/Alberto-Codes/docvet
• uses — Alberto-Codes/docvet@v1
• run — pip install griffe
• Layer — Check

Before you rely on it

• It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the docvet mcp server does with a few real requests.

Choosing this one

Plenty of developer tooling servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Docvet's toolset — repo, uses, run and 1 more — is a fair guide to whether it matches your workflow. It is maintained by Alberto-Codes; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.