Is your API documentation ready for AI agents?

AI coding agents such as Claude Code, Cursor, and GitHub Copilot increasingly discover APIs, interpret documentation, and generate integration code on developers’ behalf, making documentation accessibility important for both humans and automated tools. Documentation can hinder agents when it lacks machine-readable discovery tools such as llms.txt files and structured sitemaps, does not provide Markdown or Markdown content negotiation, or contains unstable URLs, broken links, excessive page boilerplate, and inconsistent content formats. Agent Score evaluates documentation sites from 0 to 100 across these technical factors and publishes comparative results, with Claude Code’s documentation scoring 98 for features including llms.txt and Markdown support, while one unnamed cloud infrastructure site scored 35 due to significant discovery and format shortcomings. The scoring system is presented as a diagnostic tool rather than a measure of API quality or a guarantee of integration success, offering detailed checks and prompts intended to help teams improve agent-readiness while preserving the conventional developer documentation experience.

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