Summary
This server connects Claude to docvet, a Python docstring quality checker that goes beyond style linting. It runs presence checks (does a docstring exist), enrichment checks (are all sections like Raises, Yields, Attributes documented), freshness checks (has the code changed since the docstring was written), and coverage checks (will mkdocs see this file). You'd reach for this when pair programming with Claude on Python codebases where you want it to catch stale or incomplete docstrings before they pollute the AI's context window. The server exposes check, fix, and config operations so Claude can validate docstrings after modifying functions or scaffold missing sections automatically.
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docvet
Better docstrings, better AI.
Why docvet?
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:
| Layer | Check | ruff | interrogate | pydoclint | docvet |
|---|---|---|---|---|---|
| 1. Presence | "Does a docstring exist?" | -- | Yes (unmaintained) | -- | Yes |
| 2. Style | "Is it formatted correctly?" | Yes | -- | -- | -- |
| 3. Completeness | "Does it have all required sections?" | -- | -- | Partial | Yes |
| 4. Accuracy | "Does it match the current code?" | -- | -- | -- | Yes |
| 5. Rendering | "Will mkdocs render it correctly?" | -- | -- | -- | Yes |
| 6. Visibility | "Will mkdocs even see the file?" | -- | -- | -- | Yes |
pydoclint covers 3 structural categories (Args, Returns, Raises). docvet's enrichment alone has 20 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 detection), griffe rendering compatibility, and mkdocs coverage: 31 rules across 5 checks, in territory no other tool touches.
Quickstart | GitHub Action | Pre-commit | Configuration | AI Agent Integration | Docs
What It Checks
Presence (existence) -- 2 rules:
missing-docstring overload-has-docstring
Enrichment (completeness) -- 20 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
Freshness (accuracy) -- 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.
[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: v1.2.0
hooks:
- id: docvet
For griffe rendering checks, add the optional dependency:
repos:
- repo: https://github.com/Alberto-Codes/docvet
rev: v1.2.0
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@v1
Select specific checks or pin a version:
- uses: Alberto-Codes/docvet@v1
with:
checks: 'enrichment,freshness'
docvet-version: '1.9.0'
python-version: '3.13'
For griffe rendering checks, install griffe before running docvet:
- uses: actions/setup-python@v6
with:
python-version: '3.12'
- run: pip install griffe
- uses: Alberto-Codes/docvet@v1
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 | Detect stale docstrings via git |
docvet freshness --mode drift | Sweep for long-stale docstrings 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 checking catches the accuracy gap that stale docs create, and its enrichment rules ensure the docstring sections that agents use as context are complete. 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:
[](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
Featured
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →Categories
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UpdatedMay 8, 2026
