Claude Code vs Cursor vs Codex: Which Coding Agent Writes the Best API Docs?

Your coding agent can scaffold a REST endpoint, write tests, and open a PR — all without you touching the keyboard. But can it write good API documentation? We ran the same task through three leading coding agents — Claude Code, Cursor, and Codex (OpenAI) — and asked each one to document a small API from source code alone. Here's what we found, and what it tells you about where AI-assisted documentation is heading.

A Node.js Express API with six endpoints: auth, users (CRUD), and a webhook receiver. No OpenAPI file. No existing docs. Just source code and a one-line prompt:

"Read this codebase and document the API."

We measured: completeness (were all endpoints captured?), accuracy (did params, types, and response shapes match the code?), structure (was the output usable without heavy editing?), and MCP-readiness (could the output power an AI agent tool call?).

Completeness: ★★★★★ All six endpoints, all params, correct auth headers.

Accuracy: High. Claude Code reads the actual route handlers and infers types from usage — it caught an optional?include_deleted=true query param that wasn't in any README.

Structure: Good. Output was Markdown with clear H2 headings per endpoint, request/response examples, and a note about the webhook HMAC signature requirement.

MCP-readiness: With DocStar wired in, Claude Code's output maps directly to hosted MCP tool definitions — no reformatting required. The search_, get_, and call_ tools are populated from the same structured data the agent just wrote.

Verdict: Best overall. Strongest at reading implicit context (middleware, shared validators) that other agents miss.

Completeness: ★★★★☆ Caught five of six endpoints; missed the webhook receiver because it was in a separate routes/webhooks.js file not imported from the main router.

Accuracy: Solid on the endpoints it did catch. Cursor's strength is inline editing — it's fast and the output slots neatly into an existing docs file.

Structure: Very good if you already have a docs template. Cursor respects your existing format and fills it in. Without a template, the output is flat Markdown.

MCP-readiness: Requires an intermediate step. Cursor's output needs to be imported into a documentation platform before it can power MCP tools. There's no native path from Cursor → hosted MCP server.

Verdict: Best for teams that already have a docs framework and want an agent to fill gaps. Weaker for greenfield documentation.

Completeness: ★★★☆☆ Four of six endpoints. Missed the webhook receiver and merged two user endpoints into one.

Accuracy: Mixed. Codex hallucinated a PUT /users/:id/role endpoint that doesn't exist, and got the response shape of the auth endpoint wrong (listed access_token instead of token ).

Structure: The output read more like a comment block than a developer reference. Needed substantial editing before it was publishable.

MCP-readiness: Not applicable without further tooling.

Verdict: Codex is a strong code-generation tool but not yet a reliable documentation writer. Use it for logic, not for consumer-facing API reference.

The results reveal three things that separate good agent-written docs from bad ones:

Even with Claude Code's strong output, there's a gap between "agent wrote the docs" and "those docs power an MCP server your users can connect to." That gap is a platform. DocStar closes it. Paste one prompt in Claude Code, Cursor, or Codex — DocStar reads the output (or reads the source directly), structures it, and publishes:

All kept in sync with your repo. No separate documentation editor. No OpenAPI file required upfront. MSG91 ( docs.msg91.com ) runs on DocStar today.

Your API should be readable by humans and AI agents. Most APIs today are neither. Fix both in one step.

DocStar publishes AI-first API docs, hosted MCP servers, and developer CLIs from one coding-agent prompt. Free to start.