docvet 1.15.0

Comprehensive docstring quality vetting for Python projects

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:

[![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