Autopia MCP server

Autopia

Autopia empirically tests HTTP APIs that ship an OpenAPI spec, discovers how they actually behave, verifies every finding by reproducing it, and serves the result as a versioned, queryable knowledge base that AI agents consult over MCP, and that a Claude Code plugin injects into an agent's context at the moment it is about to call one of those APIs.

It is the MVP of the "Discrimen Compendium" described in docs/compendium-spec.txt (the blueprint) and docs/compendium-stack.txt (stack choices). Read the spec's glossary first; the code uses those exact terms. HANDOFF.txt holds the running state and decisions.

Live: the hosted server is at https://autopia-mcp.onrender.com/mcp (per-user keys, see Identity); the database is a managed Postgres on Neon; a GitHub Actions schedule refreshes every target nightly (rules only) and weekly (with the model pass). The catalogue currently holds 16 APIs, including GitHub REST, the npm registry, PyPI, the Anthropic models endpoints, the Open-Meteo family, weather.gov, Binance, Frankfurter, Open Library, PokeAPI and Nager.Date.

What an agent gets

A behavior sheet per API: Markdown generated from verified claims only, ordered by value. Where the live API contradicts its own spec comes first, then rules the spec never states, then behavior common to every endpoint, then per endpoint: what responses look like, error-body shapes, pagination, validation, auth. Every line ends with the trial ids that back it. Examples of what the sheets record, all reproduced on the live APIs:

(200, empty list), while issues rejects the same value with a 422 that spells out the format.

• GitHub silently applies since=not-a-date on commits as a filter that matches nothing

tier instead of 5000/hour; per_page is clamped to 100 (proved by the Link header).

• GitHub accepts a missing token on public reads but serves the request from the 60/hour

its score-weight parameters.

• npm search accepts size=2500 against a documented maximum of 250, and does not type-check

as a cursor but a 404 as a path.

• The Anthropic /v1/models cursors must name an existing model id; an unknown id is a 400

The pipeline

discover -> exercise -> aggregate -> cross-ref -> verify -> write (+ diff)
 spec       TRIALS      rules +      spec +       evidence   CLAIMS
 fetch,     (no LLM)    shape rules  siblings +   check +    + DIFFS
 hash,                  + one LLM    self-        reproduce
 trim                   pass         consistency

Shortened. The full README is on GitHub.