API Docs Maintenance Workflow

Docs that match what the API actually does — tested every deploy, never stale.

Updated 2026-04-18
Tycoon solution

AI CTO owns a docs-that-test-themselves workflow. OpenAPI spec auto-generated from your FastAPI/Express/Fastify routes. Mintlify/ReadMe syncs from the spec on every deploy. Code samples get executed against a sandbox API in CI. Breaking changes in the spec trigger docs updates + customer notifications. Docs that don't match reality become impossible.

How it runs

  1. Auto-generate OpenAPI spec

    AI CTO generates the OpenAPI 3.1 spec from your code: FastAPI (Pydantic models → spec), Express+Zod (zod-to-openapi), Fastify (@fastify/swagger), NestJS (built-in), Go (huma, swag). Spec commits to /openapi.yaml on every merge. Becomes your single source of truth.

  2. Sync to docs platform

    Mintlify, ReadMe, Redocly, or Scalar pulls from /openapi.yaml on deploy. Endpoint pages, schema definitions, and request/response examples stay in sync automatically. Zero manual 'update the docs' tickets.

  3. Test code samples in CI

    Every code sample in docs (curl, Node, Python, Go, Ruby) runs in CI against a sandbox deployment. Breaking changes detected before merge. Result: docs you can copy-paste and they actually work. Most teams find 10-20 silently-broken samples on first run.

  4. Draft explanations for new endpoints

    When a new endpoint appears in the spec, AI Head of Content drafts the prose explanation: what it's for, when to use it, common patterns, gotchas, linked endpoints. Pulls context from the PR description and linked Linear issue. You review in 2 minutes instead of writing from scratch.

  5. Flag breaking changes

    Spec diff between versions identifies breaking changes: removed endpoints, required fields added, response schema narrowed. Blocks merge until migration docs + customer notification plan are in place. Enterprise customers get 30-day notice; public API gets 90-day.

  6. Track doc freshness

    AI CTO scans docs for stale signals: hardcoded dates >6 months old, 'coming soon' tags that never shipped, curl examples that 404 against current API, screenshots of old UI. Flags in a weekly freshness report. Stale docs surface before customers find them.

  7. Answer docs-adjacent support tickets

    When a support ticket says 'docs don't match reality', AI Head of Content parses the ticket, identifies the discrepancy, fixes the docs if wrong or clarifies the expected behavior, responds to the customer with the update. Converts docs complaints into docs improvements.

Who runs it

  • hire/ai-cto
  • hire/ai-head-of-content

What you get

  • OpenAPI spec always matches deployed API
  • Docs pages sync automatically on every deploy
  • Code samples tested continuously (zero stale curls)
  • Breaking changes caught before merge
  • New endpoints get prose explanation without writer in the loop
  • Docs-related support tickets drop 60-80%
  • Developer churn from 'docs didn't match' drops toward zero
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Our API is legacy REST with no OpenAPI spec and messy handlers. Can this even start?

Yes, with a bootstrap phase. AI CTO introspects your API via a crawler: every route in your router, every response captured from running traffic, every status code observed, every query parameter inferred. Assembles a best-guess OpenAPI spec (70-85% accurate) that you then refine. Takes 1-2 weeks of review to get to production-quality, but it's front-loaded — once the spec exists, the automated workflow takes over. Most teams report that the bootstrap alone caught 15-30 inconsistencies they didn't know about.

We use GraphQL, not REST. Does this still apply?

Yes, different tooling. GraphQL schemas are already introspectable via SDL. AI CTO uses your schema + @deprecated directives + operation definitions to drive the docs. Testing moves from curl samples to operation samples with expected responses. Docs platforms for GraphQL (GraphQL Inspector, Apollo Studio, Hashnode GraphQL) integrate the same way. Breaking change detection is actually more accurate for GraphQL because the SDL is typed.

What about internal tools / internal APIs where docs are for engineers, not customers?

Same workflow, different polish level. Internal docs get auto-generated from the spec but skip the prose-writing step (engineers can read the spec fine). Internal endpoints stay in a separate namespace on the docs site (docs.internal.yourcompany.com) with auth gating. The value for internal: new engineers onboard in hours instead of days because the docs are actually complete.

Our API has authentication, webhooks, and async callbacks. Auto-generation misses context for those.

Auto-generation handles the schema; context is layered on top. AI Head of Content maintains the 'conceptual docs' (authentication flows, webhook signatures, idempotency rules, rate limits, error handling patterns) as long-form Markdown that references the auto-generated endpoint pages. When endpoints change, the conceptual pages get checked for affected content and flagged for review. Structure: auto-generated = what; human-maintained = why and how.

Can it handle SDKs for multiple languages — keeping Node/Python/Go SDKs in sync with the REST API?

Yes, via code generation + test matrix. OpenAPI Generator produces SDKs from the spec in 40+ languages. AI CTO runs a CI job that: (1) regenerates SDKs on every spec change, (2) runs the test suite for each SDK against the sandbox API, (3) flags SDK breaking changes separately from API breaking changes. SDKs publish to npm/PyPI/Go modules on every API release. Prevents SDK drift — the failure mode where the Python SDK is 6 months behind the API because nobody remembered to regenerate it.

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