docvet

provenance:github:Alberto-Codes/docvet

WHAT THIS AGENT DOES

docvet is a tool designed to improve the quality and accuracy of docstrings in Python code, ensuring they are present, complete, up-to-date, and render correctly. Developers and teams can use docvet to maintain high-quality documentation, which is crucial for code maintainability, collaboration, and AI integration. It goes beyond basic style checks by verifying the completeness and accuracy of docstrings, detecting staleness, and ensuring compatibility with documentation rendering tools like mkdocs. docvet helps prevent documentation from becoming outdated or inaccurate, leading to more reliable and understandable codebases. The tool's comprehensive rule set and integration options make it a valuable asset for any Python project. It provides a robust solution for maintaining documentation standards and improving the overall developer experience. Finally, docvet supports AI agent integration, enabling automated documentation improvements.

PROBLEM IT SOLVES

docvet solves the problem of outdated, incomplete, or inaccurate docstrings, which can lead to confusion, errors, and difficulty in understanding and maintaining code. Developers often rely on simpler tools that only check style, leaving gaps in documentation quality. docvet fills this gap by providing a comprehensive solution that ensures docstrings are present, accurate, and up-to-date, saving developers time and reducing the risk of errors.

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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](#quickstart)** | **[GitHub Action](#github-action)** | **[Pre-commit](#pre-commit)** | **[Configuration](#configuration)** | **[AI Agent Integration](#ai-agent-integration)** | **[Docs](https://alberto-codes.github.io/docvet/)**

## 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

```bash
pip install docvet && docvet check --all
```

For optional griffe rendering checks:

```bash
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.

```toml
[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`:

```yaml
repos:
  - repo: https://github.com/Alberto-Codes/docvet
    rev: v1.2.0
    hooks:
      - id: docvet
```

For griffe rendering checks, add the optional dependency:

```yaml
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:

```yaml
- uses: Alberto-Codes/docvet@v1
```

Select specific checks or pin a version:

```yaml
- 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:

```yaml
- 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](https://alberto-codes.github.io/docvet/ai-integration/).

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

```markdown
## Docstring Quality

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

Recommended `pyproject.toml` configuration:

```toml
[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](https://arxiv.org/abs/2404.03114)
- Comment density [improves code generation by 40-54%](https://arxiv.org/abs/2402.13013)
- Misleading comments [reduce LLM fault localization accuracy to 24.55%](https://arxiv.org/abs/2504.04372)
- Performance [drops substantially without docstrings](https://arxiv.org/abs/2508.09537)

As the [2025 DORA report](https://cloud.google.com/resources/content/2025-dora-ai-assisted-software-development-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](https://stackoverflow.blog/2026/02/04/code-smells-for-ai-agents-q-and-a-with-eno-reyes-of-factory).

docvet's freshness checking catches the accuracy gap that stale docs create, and its enri

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First discoveredMar 23, 2026

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