docverity
Outputs results in GitHub format to create inline annotations on pull request changed lines for documentation drift.
Can be integrated into GitHub Actions workflows to automatically check documentation claims on pull requests and fail builds on drift.
Click on "Install 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., "@docveritycheck docs for drift after updating the CLI flags"
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.
Docverity
Catch documentation that lies about your code.
Docverity reads your docs, extracts the concrete claims they make about your codebase, and checks each one against the actual source. When a documented flag gets renamed, a config default changes, or a referenced file is deleted, your docs silently start lying. Docverity turns that into a failing check, in CI, before your users hit it.
Existing tools run the code blocks in your docs (doctest, mdbook test) or
help you author and host docs. None of them verify that the prose still tells
the truth about the code. That is the gap Docverity fills.
Two engines
Docverity ships with two complementary engines:
Reference checker — deterministic, no API key, instant. Catches docs that mention files, CLI flags, environment variables, or symbols that no longer exist anywhere in the source.
Claim verifier — LLM-backed, catches prose-level semantic drift: "the default timeout is 30s", "this returns a list", "set
FOO=barto enable X". Runs whenANTHROPIC_API_KEY(orANTHROPIC_AUTH_TOKEN) is set.
Works free out of the box. Gets smarter with a key.
Related MCP server: ws-mcp
Install
npm install -g docverityOr run without installing:
npx docverityUsage
From your repo root:
docverityBy default it checks README.md and every .md file under docs/. Pass files
explicitly to narrow the scope, and use -C to point at another root:
docverity -C path/to/repoSkip the LLM engine for a fast, deterministic-only run:
docverity --no-llmOptions
Flag | Description |
| Deterministic checks only; no API calls. |
| Model for the LLM engine (default |
| Minimum confidence (0..1) to fail the build on. Default |
| Also fail on unverifiable claims. |
|
|
Docverity exits non-zero when it finds drift above the confidence threshold, so
it fails CI the way a linter would. Exit codes: 0 clean, 1 drift found,
2 a configuration error (e.g. an invalid --fail-confidence or a missing doc
file) so a typo can never mask real drift with a green build.
In CI (GitHub Actions)
name: docs
on: [pull_request]
jobs:
docverity:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with:
node-version: 22
- run: npx docverity --format github
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}With --format github, drifted claims appear as inline annotations on the
changed lines of the pull request.
Use it from an agent (MCP)
Docverity ships an MCP server so a coding agent can check docs as a tool and fix drift in the same turn it changed the code. Add it to your MCP client:
{
"mcpServers": {
"docverity": {
"command": "npx",
"args": ["docverity", "mcp"]
}
}
}This exposes one tool, check_docs, which returns each drifted claim with its
file, line, the stale text, code evidence, a confidence score, and a suggested
fix the agent can apply directly. It runs the deterministic engine by default
(fast, free, no key); pass llm: true to also verify prose claims.
A SKILL.md is included so the agent knows when to reach for it
(after editing code, before a release, when asked whether the docs are correct).
How it works
Extract — parse each doc into atomic claims (a flag, a path, an env var, a symbol, or a prose assertion).
Locate — search the source tree (via ripgrep when available) for evidence of each claim. Documentation files are excluded from evidence: a claim must be backed by code, not by the docs restating it.
Verify — the reference engine checks for hard evidence; the LLM engine judges prose claims against the located evidence and returns ok / drifted / unverifiable with a specific reason.
Report — pretty output, machine-readable JSON, or GitHub annotations.
License
MIT
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