Docvet

Name: Docvet
Author: Alberto-Codes

by Alberto-Codesversion 3.0.1i★ –iRuns on your computer

Registry nameio.github.Alberto-Codes/docvet

Install

Overview

Description from the maker (original English)

Docstring quality vetting for Python -- enrichment, freshness, coverage, and presence checks

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "docvet": {
      "command": "uvx",
      "args": [
        "docvet"
      ]
    }
  }
}

Run this in your terminal

claude mcp add docvet -- uvx docvet

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "docvet": {
      "command": "uvx",
      "args": [
        "docvet"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "docvet": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "docvet"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Alberto-Codes, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32558 servers in this category
Published to the registry more recently than 71% of the other 32558 servers in this category
Basic checks equal to or better than 100% of the other 32558 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Docvet safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (3.0.1) reached the registry 15 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, FactLot

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

docvet

Better docstrings, better AI.

Why docvet?

docvet combines docstring presence metrics, structural checks, history-based staleness signals, and renderer diagnostics in one CLI, with editor and agent interfaces. These checks help reviewers find documentation gaps; they do not prove that prose describes runtime behavior correctly.

Layer	Check	ruff	interrogate	pydoclint	docvet
1. Presence	"Does a docstring exist?"	Yes (D rules, opt-in)	Yes, with coverage metrics	Skips absent docstrings	Yes, with coverage metrics
2. Style	"Is it formatted consistently?"	Yes (D rules, opt-in)	--	Some parsing/style checks	Limited format checks
3. Completeness	"Do sections and code structure agree?"	Partial (D rules; DOC rules in preview)	--	Arguments, returns, yields, raises, attributes, type hints	AST-based section and agreement checks
4. Accuracy	"What needs review for staleness?"	Structural checks above	--	Structural checks above	Git diff/blame heuristics; no semantic proof
5. Rendering	"Are there parser warnings?"	--	--	--	Griffe diagnostics
6. Visibility	"Is package discovery incomplete?"	--	--	--	Missing `__init__.py` checks

Comparison reviewed 2026-10-21 against primary documentation: Ruff's D103 presence rule and DOC501 exception completeness rule (requires --preview and rule selection), pydoclint's supported checks and styles, and interrogate's coverage documentation. Capabilities depend on enabled rules and configuration; -- means no corresponding check is described in these sources.

pydoclint checks arguments, returns, yields, raises, class attributes, and type hints across Google, NumPy, and Sphinx styles. docvet's enrichment alone has 21 rules, including Raises, Yields, Receives, Warns, Attributes, Examples, cross-references, parameter agreement, and more. Add presence (coverage metrics + threshold enforcement), freshness (git diff/blame staleness signals), griffe rendering compatibility, and mkdocs coverage: 32 rules across 5 checks. Rule totals measure catalog entries, not equivalent coverage between tools.

Structural agreement checks compare documented elements with code structure. Freshness uses change history to suggest review: code can change without invalidating prose, and simultaneous code/docstring edits can still leave incorrect prose. Neither establishes semantic correctness.

What It Checks

Presence (existence) -- 2 rules: missing-docstring overload-has-docstring

Enrichment (completeness) -- 21 rules: missing-raises missing-returns missing-yields missing-receives missing-warns missing-deprecation missing-param-in-docstring extra-param-in-docstring missing-other-parameters missing-attributes undocumented-init-params missing-typed-attributes missing-examples missing-cross-references extra-raises-in-docstring extra-yields-in-docstring extra-returns-in-docstring missing-return-type trivial-docstring prefer-fenced-code-blocks scaffold-incomplete

Freshness (staleness signals) -- 5 rules: stale-signature stale-body stale-import stale-drift stale-age

Griffe (rendering) -- 3 rules: griffe-unknown-param griffe-missing-type griffe-format-warning

Coverage (visibility) -- 1 rule: missing-init

Quickstart

pip install docvet && docvet check --all

For optional griffe rendering checks:

pip install docvet[griffe]

Example output:

src/mypackage/helpers.py:1: missing-docstring Module has no docstring [required]

src/mypackage/utils.py:42: missing-raises Function 'parse_config' raises ValueError but has no Raises section [required] src/mypackage/models.py:15: stale-signature Function 'process' signature changed but docstring not updated [required] src/mypackage/api.py:1: missing-init Package directory missing init.py (invisible to mkdocs) [required]

Configuration

Configure via [tool.docvet] in your pyproject.toml. All checks run and print findings. Checks listed in fail-on cause a non-zero exit code; unlisted checks are treated as warnings.

A check in fail-on that cannot run — most often griffe without the docvet[griffe] extra — never certified the gate you configured, so docvet reports it on stderr and exits 1. With --format json the run object reports status: "unavailable" and an unavailable_checks array, which is what distinguishes it from a gate that found problems. Set fail-on-unavailable = false (or pass --no-fail-on-unavailable) to warn and exit 0 instead. Unavailable checks that nothing gates on stay a quiet skip — though fail-on membership is not the only thing that gates: a min-coverage floor gates presence without naming it there.

[tool.docvet]

exclude = ["tests", "scripts"] fail-on = ["griffe", "coverage"]

[tool.docvet.freshness] drift-threshold = 30 age-threshold = 90

Pre-commit

Add to your .pre-commit-config.yaml:

repos:

repo: https://github.com/Alberto-Codes/docvet rev: v3.0.0 hooks:
id: docvet

For griffe rendering checks, add the optional dependency:

repos:

repo: https://github.com/Alberto-Codes/docvet rev: v3.0.0 hooks:
id: docvet additional_dependencies: [griffe]

GitHub Action

Add docvet to your GitHub Actions workflow — findings appear as inline annotations on your PR:

- uses: Alberto-Codes/docvet@v3.0.0

Select specific checks or pin a version:

- uses: Alberto-Codes/docvet@v3.0.0

with: checks: 'enrichment,freshness' docvet-version: '3.0.0' python-version: '3.13'

The griffe rendering check needs no setup: the action installs docvet[griffe], pinned or not, so every check the checks input offers is available. Note that docvet releases before 1.7.0 declare that extra without an upper bound, so pinning one installs whatever griffe publishes at the time rather than a version docvet was released against.

Behavior change — this can turn a passing build red.

Earlier releases installed plain docvet, so the griffe check was skipped and contributed zero findings. It now runs. determine_run_outcome (src/docvet/reporting.py) returns exit code 1 as soon as any check listed in fail-on reports findings, so if your pyproject.toml has griffe in [tool.docvet] fail-on, your build goes from green to failing with no change on your side. This repository's own ci.yml docvet job is exactly such a consumer.

These are not new problems — it is the check finally running on docstrings that were always broken. To get back to green, fix the griffe findings or remove griffe from fail-on.

AI Agent Integration

For tool-specific integration snippets, see the full AI Agent Integration guide.

Add docvet to your AI coding workflow. Drop this into your CLAUDE.md, .cursorrules, or agent configuration:

## Docstring Quality

After modifying Python functions, classes, or modules, run docvet check and fix all findings before committing.

Recommended pyproject.toml configuration:

[tool.docvet]

fail-on = ["enrichment", "freshness", "coverage", "griffe"]

Subcommand Quick Reference

Command	Description
`docvet check`	Run all enabled checks (default: git diff files)
`docvet check --all`	Run all checks on entire codebase
`docvet check --staged`	Run all checks on staged files only
`docvet presence`	Check for missing docstrings with coverage metrics
`docvet enrichment`	Check for missing docstring sections
`docvet freshness`	Flag potentially stale docstrings via git
`docvet freshness --mode drift`	Review potential staleness via git blame
`docvet coverage`	Find files invisible to mkdocs
`docvet griffe`	Check mkdocs rendering compatibility
`docvet fix`	Scaffold missing docstring sections
`docvet fix --dry-run`	Preview scaffolding changes without writing files
`docvet config`	Show effective configuration with source annotations
`docvet lsp`	Start LSP server for real-time editor diagnostics
`docvet mcp`	Start MCP server for AI agent integration

Better Docstrings, Better AI

AI coding agents rely on docstrings as context when generating and modifying code. Agents modify code but often leave docstrings stale, and research shows stale or incorrect documentation is actively harmful, worse than no docs at all:

Incorrect docs degrade LLM task success by 22.6 percentage points
Comment density improves code generation by 40-54%
Misleading comments reduce LLM fault localization accuracy to 24.55%
Performance drops substantially without docstrings

As the 2025 DORA report puts it: "AI doesn't fix a team; it amplifies what's already there." The only signal correlating with AI productivity is code quality.

docvet's freshness heuristics flag potential staleness for review, and its enrichment rules check supported docstring sections against code structure. Run docvet check in your CI, pre-commit hooks, or agent toolchain.

Badge

Add a badge to your project to show your docs are vetted:

[![docs vetted | docvet](https://img.shields.io/badge/docs%20vetted-docvet-purple)](https://github.com/Alberto-Codes/docvet)

Used By

Are you using docvet? Open a pull request to add your project here.

License

MIT -- see LICENSE for details.

mcp-name: io.github.Alberto-Codes/docvet

Docvet

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

FactLot

README from the maker

docvet

Why docvet?

What It Checks

Quickstart

Configuration

Pre-commit

GitHub Action

AI Agent Integration

Subcommand Quick Reference

Better Docstrings, Better AI

Badge

Used By

License