Notes on documentation beside the API code

API documentation often starts as a separate document. Someone describes the available requests, adds a few examples, and updates the page when they remember. That works until the code changes and the document does not. Generating API documentation from code brings those two things closer together.

The idea is straightforward: reuse information already present in your application to describe its public interface. The result can be a browsable reference showing available endpoints, accepted inputs, and expected responses. It saves repetitive work, but it does not remove the need to explain what an API is for.

§ What the generator reads

An API endpoint is an address that accepts a particular kind of request. Its implementation usually contains useful facts: the HTTP method, the URL path, input fields, validation rules, and sometimes response types. A documentation generator collects those facts and turns them into a consistent description.

Depending on the programming language and framework, the generator may read route definitions, type declarations, structured comments, or annotations added beside the code. Some frameworks expose this information directly. Others need extra configuration.

A common output is an OpenAPI document. OpenAPI is a standard format for describing HTTP APIs. A documentation viewer can turn that document into readable pages, and other tools can use it to generate client libraries or validate requests. The OpenAPI document is the description; the viewer is the presentation.

§ Start with one small endpoint

Imagine an endpoint that creates a note. It accepts a title and some text, then returns the saved note with an identifier. Before generating anything, make those expectations explicit in the code.

• Define which fields are required and which are optional.
• Describe field types and meaningful limits, such as maximum title length.
• Declare the successful response structure.
• Document likely errors, including invalid input and missing authentication.

Run your framework’s documentation tool and inspect the result. Could someone send a valid request without reading the implementation? Could they recognize a failed request? Those questions are more useful than whether the generated page looks complete.

§ Generated facts still need human explanations

A field named status may be a string, but that does not tell a beginner which values are allowed or when they change. A successful response definition also cannot explain whether repeating a request creates a duplicate.

Add short explanations where behavior matters. Describe authentication, pagination, rate limits, and retry behavior if your API uses them. Include examples that reflect actual accepted input, not imaginary values that merely look plausible.

Generated documentation is only as reliable as its source information. A generator may miss responses produced by middleware, dynamically constructed routes, or validation rules that are not exposed to it. Review the output rather than assuming that generated means correct.

§ Where AI assistance fits

An AI assistant can draft endpoint summaries, explain unfamiliar schemas, or suggest missing examples. Give it the relevant code and generated specification, then ask it to identify uncertainty rather than invent behavior. Treat its output as a draft to check against the implementation.

If your documentation workflow uses an OpenAI SDK, Vana API provides an OpenAI-compatible endpoint at https://api.vanaapi.com. It also supports the Anthropic Messages and Gemini native protocols. This can support an AI-assisted writing step, but it does not replace the framework tool that extracts your API structure. Create a Vana API account if you want to try that step, with usage billed per real token and no monthly fee.

§ Keep the reference close to releases

Generate documentation during your build or release process, and review specification changes alongside code changes. Where practical, test example requests against a safe test environment. Never include production credentials or real customer data in examples.

The useful goal is modest: a reference that changes when the interface changes, with enough human explanation to make it understandable. Start with one endpoint, check what the generator actually captures, and build from there.