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Claude/ChatGPT Prompt to Generate OpenAPI-Friendly Endpoint Docs

Generate clear, copy-ready endpoint docs in Stripe-style quality that paste straight into an OpenAPI spec or API reference.

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Your Prompt
prompt.txt

                                

What this prompt does

This prompt generates complete, copy-ready endpoint documentation in a Stripe-style reference quality. It casts the assistant as a senior API technical writer and takes context variables describing the endpoint — [method], [path], [service] — and the [auth_scheme]. The deliverables are a one-sentence purpose plus the authentication requirement, a request parameters table (name, type, required, description), a request example as both a curl command and a JSON body, the response schema with field descriptions, response examples for 200 plus the two most common error codes, and rate-limit and idempotency behaviour. The output is Markdown crisp enough to drop into an OpenAPI spec or reference page.

The structure works because inconsistent endpoint docs are what turn an integration into a week of back-and-forth. By forcing a fixed shape — purpose, params table, dual request examples, response schema, error examples, and rate-limit/idempotency notes — the prompt matches a known-good reference style so consumers rarely have to ask how auth or errors actually work. Pasting your real validation rules is what keeps the required column and error examples accurate rather than invented.

When to use it

  • Documenting REST endpoints you're shipping for client or partner integrations
  • When inconsistent endpoint docs are causing repeated integration questions
  • When you want Stripe-quality reference pages without writing them by hand
  • When docs need both a curl example and a JSON body for each request
  • When error responses and idempotency behaviour keep getting asked about
  • When you're populating or backfilling an OpenAPI spec

Example output

You get crisp Markdown for one endpoint: a one-sentence purpose and auth requirement, a parameters table with name, type, required, and description columns, a request shown as both a curl command and a JSON body, the response schema with each field described, response examples for a 200 and the two most common error codes, and a note on rate-limit and idempotency behaviour. It's formatted to drop into an OpenAPI spec or a Stripe-style reference page with minimal editing. The error examples are easy to overlook but pull the most weight, because integrators spend more time handling the failure cases than the happy path, and docs that show real error bodies save them from guessing at your error shape.

Pro tips

  • Paste your real validation rules so the required column and error examples are accurate rather than invented
  • Set [auth_scheme] precisely ("Bearer token (OAuth 2.0)") so the authentication requirement matches your actual implementation
  • Give the true [method] and [path] so the curl example and route are correct out of the box
  • Verify the rate-limit and idempotency notes against your real behaviour, since the model will guess if you don't specify
  • Run the generated curl command against a test environment to confirm the example actually works
  • Keep one endpoint per run; documenting several at once dilutes the per-endpoint detail

Frequently Asked Questions

Will the parameters table be accurate?
It's only as accurate as the input you give. Paste your real validation rules so the required column and types reflect your actual implementation. Without them, the model infers parameters, which can produce a plausible but wrong table you'd have to correct.
Does it include both curl and JSON examples?
Yes, the request is shown as both a curl command and a JSON body. That dual format covers consumers who test from the terminal and those who build the request in code, reducing the back-and-forth that inconsistent docs usually cause.
Are the rate-limit and idempotency notes reliable?
The prompt asks for them, but the model will guess if you don't specify your real behaviour. Provide your actual rate limits and idempotency rules, or verify the generated notes against your implementation before publishing the docs.
Can I document several endpoints at once?
You can, but per-endpoint detail suffers when you batch them. Running one endpoint per invocation keeps the parameters, examples, and error cases sharp. For a full spec, loop the prompt over each endpoint rather than asking for many at once.
Engr Mejba Ahmed

Need this built for real?

Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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