docvet
This server lets you analyze Python docstring quality via docvet and get rule guidance.
Run docvet checks on a Python file or directory using
docvet_check.Select specific checks (presence, enrichment, freshness, coverage, griffe); defaults to all except freshness.
Get structured results as JSON with findings, summary statistics, and optional presence coverage data.
Handle invalid inputs gracefully — bad paths, unknown check names, or malformed configs return structured errors instead of crashing.
List all docvet rules with
docvet_rules, including check module, description, category, fix guidance, and format examples.
Analyzes git diff and blame history to detect stale or outdated docstrings, helping maintain documentation accuracy as code changes.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@docvetCheck my Python files for docstring issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: MCP SDD Server
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 --allFor 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 = 90Pre-commit
Add to your .pre-commit-config.yaml:
repos:
- repo: https://github.com/Alberto-Codes/docvet
rev: v1.2.0
hooks:
- id: docvetFor 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@v1Select specific checks or pin a version:
- uses: Alberto-Codes/docvet@v1
with:
checks: 'enrichment,freshness'
docvet-version: '1.9.0'
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 infail-onreports findings, so if yourpyproject.tomlhasgriffein[tool.docvet] fail-on, your build goes from green to failing with no change on your side. This repository's ownci.ymldocvet 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
griffefromfail-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 |
| Run all enabled checks (default: git diff files) |
| Run all checks on entire codebase |
| Run all checks on staged files only |
| Check for missing docstrings with coverage metrics |
| Check for missing docstring sections |
| Detect stale docstrings via git |
| Sweep for long-stale docstrings via git blame |
| Find files invisible to mkdocs |
| Check mkdocs rendering compatibility |
| Scaffold missing docstring sections |
| Preview scaffolding changes without writing files |
| Show effective configuration with source annotations |
| Start LSP server for real-time editor diagnostics |
| 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
Available Tools
2 toolsdocvet_checkA
Run docvet checks on Python files.
Analyzes Python source files for docstring quality issues. Runs all
enabled checks except freshness by default (freshness requires git
context; griffe also excluded when not installed). When *path* is a
directory, only files within that directory tree are checked (not the
entire project). Returns a JSON object with findings, summary
statistics, and optional presence coverage data. Invalid
configuration triggers a structured error response instead of
crashing the server.
Args:
path: Path to a Python file or directory to check.
checks: Optional list of check names to run. Valid names are
``presence``, ``enrichment``, ``freshness``, ``coverage``,
``griffe``. Defaults to all except freshness.
Returns:
JSON string with ``findings``, ``summary``, and optionally
``presence_coverage`` keys. Returns an ``error`` key on
invalid path, unknown check name, or malformed configuration.
Call docvet_rules() for per-rule fix guidance and format examples.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| checks | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so: it discloses the default check set, the conditional exclusions and their reasons, the directory-scoping rule (only files in that tree, not the whole project), the return shape including optional keys, and error behavior ('Invalid configuration triggers a structured error response instead of crashing the server'). That is unusually complete behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The key behavior (defaults, exclusions, scoping) is front-loaded before the Args/Returns block, and every sentence carries information. It is somewhat long, and the Returns section partly duplicates the existing output schema, so it is efficient rather than maximally tight.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool with an output schema and no annotations, the description covers selection of checks, defaults, conditional exclusions, scoping semantics, error modes, and a sibling cross-reference. Nothing an agent needs to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, and it does: it enumerates the valid check names (presence, enrichment, freshness, coverage, griffe) that the schema leaves as an unconstrained array of strings, and defines path as a Python file or directory. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening states a specific verb and resource: 'Run docvet checks on Python files' and 'Analyzes Python source files for docstring quality issues.' It also distinguishes itself from the only sibling by routing the agent to docvet_rules() for per-rule fix guidance, so the agent can tell the two apart without opening either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains the default behavior ('all enabled checks except freshness') and the conditions that change it ('freshness requires git context; griffe also excluded when not installed'), plus a pointer to the sibling for a different task. It does not give an explicit 'do not use this when...' exclusion, which keeps it just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
docvet_rulesB
List all available docvet rules with fix guidance.
Returns the complete rule catalog with rule name, associated check
module, human-readable description, category (required or
recommended), prescriptive fix guidance, and a format example.
Returns:
JSON string with a ``rules`` array containing objects with
``name``, ``check``, ``description``, ``category``,
``guidance``, and ``fix_example`` keys.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It makes clear this is a static catalog read by describing the returned payload in detail, but it never explicitly states that the call has no side effects, is safe to repeat, or requires no configuration; the return enumeration also largely duplicates the output schema rather than adding behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The opening sentence is well front-loaded and wastes nothing. However, the multi-line 'Returns:' block enumerates keys that the output schema already defines, so a meaningful chunk of the text does not earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter catalog tool with an output schema present, the description covers purpose and payload adequately and need not explain return values further. The only real gap is the absence of guidance on how this relates to docvet_check.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is nothing to document and the baseline is 4. The description correctly implies the call is argument-free by framing it as a full catalog listing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('List') and resource ('docvet rules') and immediately adds scope ('all available', 'with fix guidance'). An agent can tell this is the rule catalog, but the description never mentions the sibling docvet_check, so differentiation must be inferred from the names alone.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no statement of when to use this tool versus docvet_check, no prerequisites, and no exclusions. A reader can guess it is a lookup helper, but nothing in the text routes the agent between the two tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v1.16.0- First observed
docvet_check - First observed
docvet_rules
TDQS
Scored across 2 tools
The two tools serve clearly distinct purposes: docvet_check executes checks and returns findings, while docvet_rules lists rule metadata and fix guidance. There is no overlap, and the check tool explicitly points to the rules tool for next steps.
Both tools use a consistent snake_case pattern with the shared 'docvet_' prefix. However, the second parts differ in form ('check' is an action, 'rules' is a resource), which is a minor deviation from a pure verb_noun convention.
Two tools are slightly under the typical 3-15 range, but each earns its place in this narrow, well-scoped docstring-checking domain. The check tool is feature-rich, so the set does not feel arbitrarily thin.
The core workflow—run checks, then look up rule guidance—is fully covered, and the check tool handles configuration errors gracefully. Minor gaps exist, such as no direct auto-fix or fix-application tool, but these are not essential for the stated purpose.
Maintenance
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