◎ Discry Score
retrain.ai.com
ai · API
D
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 99 (A)
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AI · RANK #40 OF 43

retrain.ai is Poor to agents.

Discry independently scored how well an AI agent can discover and understand the retrain.ai API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.

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SCORED UNDER RUBRIC 1.2 · A full re-launch under Discry Score 2.5 — a new behavioral instrument, not comparable to these scores — is in progress.

Discovery

45% of score · 50/100
OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Partial
llms.txtAn llms.txt index that points agents to the docs that matter.Pass
llms.txt qualityThe llms.txt is focused, current, and well under the size budget.Pass
llms-full.txtA full-text bundle agents can load in one request.Fail
AGENTS.mdAn AGENTS.md that tells coding agents how to build on the API.Fail
.well-known/mcp.jsonA discoverable MCP manifest at a well-known path.Fail
MCP registryThe API is listed in a public MCP registry.Fail
robots.txt AI directivesrobots.txt allows (or explicitly guides) AI crawlers.Pass
SitemapA sitemap so agents can enumerate the docs surface.Fail

Comprehension

55% of score · 54/100
Task-oriented descriptionsEndpoints described by what they accomplish, not just their shape.Pass
Realistic examplesRunnable, real-world request/response examples.Partial
Multi-step workflowsDocs that chain calls into complete jobs an agent can follow.Partial
Error-recovery guidanceDocumented failure modes and how to recover from them.Fail
Answer-first formatThe answer leads; preamble does not bury it.Partial
Capability boundariesClear limits — what the API can and cannot do.Fail
Naming consistencyConsistent, predictable naming across endpoints.Fail
Heading hierarchyClean heading structure agents can navigate.Partial
Markdown docsDocs available as clean markdown, not JS-rendered HTML only.Pass
Token efficiencyDocs are concise enough to fit an agent context window.Pass

What we found

  • The docs site is genuinely agent-addressable: a well-structured llms.txt indexes every page, all pages are served as native text/markdown at .md URLs, and each page carries an explicit 'For AI agents' banner pointing back to the index.
  • An agent will likely fail at the front door: the auth guide contradicts the OpenAPI spec on the exact fields to send (client_id/client_secret vs clientId/clientSecret), calls the response field access_token where the schema says token, and shows a nonstandard 'authentication: Bearer<TOKEN>' header instead of Authorization.
  • The Career Paths spec requires a field named roleName but only defines a property named roleTitle, so an agent building requests strictly from the spec gets a guaranteed 422 with no recovery guidance.
  • There is no consolidated downloadable OpenAPI spec — valid OpenAPI 3.0.2 fragments are embedded per endpoint page (with artifacts like a stray colon in the jd2skills path), forcing an agent to stitch the API together page by page.
  • Errors are bare status codes ('400: Bad request', '500: Service temporarily unavailable') with zero recovery guidance, and no rate limits or capability boundaries are documented — an agent discovers every limit by failing.

What to change

Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.

  1. 01Fix the auth documentation contradictions (clientId casing, token vs access_token, Authorization header name) — this is the highest-impact fix since every agent interaction starts there.
  2. 02Fix the Career Paths spec bug: the required field roleName does not exist in the schema (the property is roleTitle), which breaks any spec-driven client.
  3. 03Publish one consolidated OpenAPI spec at a stable URL (e.g. /openapi.json) instead of per-page embedded fragments.
  4. 04Add worked request/response examples with realistic values to each endpoint page — the docs currently contain schemas only, with placeholder examples like YOUR_CLIENT_ID.
  5. 05Document rate limits and add per-error recovery guidance (what to do on 401 token expiry, 422 field errors, 500/503), and enable llms-full.txt and sitemap.xml on the ReadMe project.

Execution coverage · INFORMATIONAL, UNSCORED

Whether an agent can actually complete a call and recover from errors is the deeper Audit layer — documented here, but not part of the Discry Score.

OAuth 2.0 client credentials (client ID + secret exchanged for a JWT bearer token, 60-minute expiry) Error format documented Rate limits documented Pagination documented Idempotency documented

Auth flow (client credentials to 1-hour JWT) is documented, and 422 validation errors have a machine-parseable schema (loc/msg/type), but all other errors are bare status codes. No rate limits, pagination, or idempotency documentation exists anywhere.

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