vgi-lint-check 0.3.0

A pydoclint-style metadata-quality linter for VGI workers.

vgi-lint

A pydoclint-style metadata-quality linter for VGI workers. It attaches to an arbitrary VGI worker, reads everything the worker contributes through DuckDB system tables, and reports quality findings — missing descriptions, undocumented columns/functions, absent or malformed example queries, untagged objects, and more — with a quality score, per-data-version baselines, and machine output for coding agents.

It works with any VGI worker regardless of implementation language (Python, Go, Rust, Java, TypeScript, …): it treats the worker as a black box and inspects only what surfaces post-attach.

Install / run

uv sync                      # haybarn is RC-only; prerelease = "allow" is set
uv run vgi-lint --help

Quick start

# Lint a local subprocess worker
uv run vgi-lint 'uv run volcano_worker.py'

# Lint a no-auth HTTP worker
uv run vgi-lint http://localhost:9009

# Machine output for a coding agent / CI
uv run vgi-lint http://localhost:9009 --format agent
uv run vgi-lint http://localhost:9009 --format json

In a worker's own repo, add a [tool.vgi-lint-check] block (see vgi-lint init) with a location, then just run vgi-lint with no arguments.

v1 supports local subprocess and no-auth HTTP workers. Authenticated (OAuth) workers are not yet supported.

What it checks

Object coverage: the catalog itself, schemas, tables, views, columns, scalar/aggregate functions, macros, settings, pragmas, and constraints. Rule families:

Family	Codes	Examples
Catalog	VGI0xx	catalog description, vgi.description_llm/_md, source_url (the worker's "listing")
Descriptions	VGI1xx	schema/table/view comment, vgi.description_llm, vgi.description_md
Discoverability	VGI12x	duplicate/short/echoed descriptions, release freshness, example richness, units (opt-in)
Columns	VGI2xx	column-comment coverage (tables and views), comment-not-echo
Functions	VGI3xx	description (+ quality), documented parameters, named arguments, examples
Tags	VGI4xx	required tag keys (opt-in), reserved-tag validity
Examples	VGI5xx	vgi.example_queries present, valid JSON, complete entries, catalog-qualified
Settings	VGI6xx	setting descriptions
Pragmas	VGI7xx	pragma descriptions
Constraints	VGI8xx	foreign-key/PK/check validity — references must point at real tables & columns
Structure	VGI11x	schema object-count cap (opt-in)
Execution	VGI9xx	example queries & CHECK constraints bind/execute (opt-in, --execute)

See RULES.md for the full per-rule reference (codes, default severities, and what each checks). Run vgi-lint rules to list them from your installed version, or vgi-lint explain VGI112 for one.

Data versions

A VGI worker can publish multiple data versions whose metadata differs. The tool can lint one or all of them and compare quality across versions:

uv run vgi-lint versions <location>            # list published versions
uv run vgi-lint <location> --data-version 2.0.0
uv run vgi-lint <location> --all-data-versions # per-version report + comparison

Baselines (grandfathering)

Adopt the linter on an existing worker without a wall of failures: record current findings as a baseline, then fail CI only on new findings. Baselines are per data version (<prefix>.<version>.json).

uv run vgi-lint <location> --baseline vgi-lint-baseline --update-baseline
uv run vgi-lint <location> --baseline vgi-lint-baseline --fail-on warning

Configuration

[tool.vgi-lint-check] in pyproject.toml (or a dedicated vgi-lint.toml):

[tool.vgi-lint-check]
location = "uv run worker.py"
select = ["ALL"]
ignore = ["VGI113"]
fail_on = "error"

[tool.vgi-lint-check.severity]
VGI201 = "error"

[tool.vgi-lint-check.options]
column_comment_min_ratio = 0.8
# Required tags are opt-in (empty by default) — set them if your workers have a
# tagging convention you want enforced:
# required_schema_tags = ["provider", "domain"]

[tool.vgi-lint-check.per-object]
"volcanos.hans.*" = { ignore = ["VGI112"] }

Precedence: defaults < pyproject.toml < vgi-lint.toml < CLI flags.

Exit codes

0 clean (or below --fail-on) · 1 config/tool error · 2 findings ≥ --fail-on (regressions only when a baseline is set) · 3 connection error.

Security / trust boundary

A subprocess LOCATION is executed as a command to launch the worker (the vgi extension spawns it). Treat location like any shell command: never pass an attacker-controlled value, and in CI never derive it from untrusted input (e.g. a fork PR title/branch). Prefer a fixed path or HTTP URL you control.

GitHub Action (reusable)

This repo ships a composite action so a worker repo can lint itself in CI with a single step — it installs uv, runs the linter (the signed vgi community extension is installed automatically), gates on fail-on, and posts the findings to the job summary. Build the worker first, then point the action at it:

# .github/workflows/ci.yml — inside a job that has already built the worker
      - name: VGI metadata quality
        uses: Query-farm/vgi-lint-check@v1
        with:
          location: "$PWD/target/release/units-worker"   # binary, command, or HTTP URL
          fail-on: warning                                 # info | warning | error | never

Gate releases harder than everyday CI — e.g. fail-on: warning on push/PR while the worker's quality is being raised, and fail-on: error (plus execute: true) in the publish workflow:

      - uses: Query-farm/vgi-lint-check@v1
        with:
          location: "$PWD/target/release/units-worker"
          fail-on: error
          execute: true        # also run example queries / CHECK constraints (VGI9xx)

Key inputs: location (required), fail-on (default error), version (pin the linter, e.g. 0.2.0), working-directory, data-version / all-data-versions, baseline, execute, format (terminal|json|agent|jsonl), config, args, summary. The action's exit-code is exposed as an output. The action ref @v1 tracks the latest v1.x of the action; pin to a tag or SHA for full reproducibility.

Development

uv run pytest               # unit tests (offline)
uv run pytest --run-live    # also run live tests against real workers
uv build                    # build sdist + wheel into dist/

Releasing (GitHub Actions → PyPI)

Publishing is automated via GitHub Actions using PyPI Trusted Publishing (OIDC — no API token secret to store):

• .github/workflows/ci.yml runs the offline test suite (Python 3.11–3.13) and a smoke build on every push/PR.
• .github/workflows/publish.yml builds, validates (twine check), and uploads to PyPI when a GitHub Release is published. It first checks that the release tag matches the version in pyproject.toml.

One-time setup on PyPI (Trusted Publisher), under the project's Publishing settings (use a "pending publisher" before the first release):

Field	Value
Owner	Query-farm
Repository	vgi-lint-check
Workflow	publish.yml
Environment	pypi

Also create a GitHub Environment named pypi in the repo settings (it gates the publish job and is referenced for the OIDC claim).

To cut a release:

# bump version in pyproject.toml, commit, then tag + create the release
git tag v0.1.0 && git push origin v0.1.0
gh release create v0.1.0 --generate-notes

The release publishing event triggers the workflow. (Prefer a token instead of OIDC? Replace the publish job's trusted-publishing step with pypa/gh-action-pypi-publish configured with password: ${{ secrets.PYPI_API_TOKEN }} and add that repository secret.)