AI Readiness for developer documentation.

Calibrated against an open rubric — see methodology →

example.com

62 / 100

AI Constrained

AI Readiness Score

Get Full Audit — $49

✗ Missing llms.txt at /llms.txt

△ TechArticle schema absent on docs pages

🔒 3 more issues affecting how AI finds your site. Get the full Audit.

AI Readiness = how well AI agents can find, understand, cite, and act on your content.

Built by Adam Kinney — co-creator of Microsoft Learn, Stripe Docs contributor.

How It Works

Audit. Fix. Monitor.

Audit

Docs Readiness Audit

The free Lightning Scan returns your AI Readiness Score in seconds — scored on the published 9-category rubric. The full Docs Readiness Audit ($49) crawls ~30 pages, ranks the structural issues by leverage, and ships a web report with the fix instructions next to each finding.

Get Full Audit →

Fix

Docs Readiness Fix Kit Pre-launch

Paste-ready remediation kit, scoped to your detected docs platform — Mintlify, Docusaurus, VitePress, GitBook, or ReadMe. Apply the fixes; re-scan to confirm the Score moved.

Monitor

AI Readiness Monitor Pre-launch

Continuous re-scans of the same pages on the same rubric. Email digest when your AI Readiness Score drops by 3+ points or any hard cap fires.

We score them all: Mintlify · Docusaurus · VitePress · GitBook · ReadMe.

What We Check

Nine dimensions of docs readiness.

What the Docs Readiness Audit measures. Full per-check detail on the published rubric.

AI Crawlability & Access

robots.txt rules for AI bots (GPTBot, ClaudeBot, CCBot, OAI-SearchBot, Googlebot), HTTP status, TLS health, redirect behavior. The catastrophic-failure gate — if this fails, the rest is moot.

Documentation Patterns

The docs-product signals AI agents look for first. TechArticle / APIReference / HowTo schema, code-block markup, and agent-readability files — llms.txt, agents.md, and .well-known/mcp.json — each scored on presence and quality. The heaviest category in the rubric (18 points) — docs-native signals carry the most weight.

Structured Data

Schema.org markup beyond docs-specific types — Article, FAQPage, Organization, the layer AI agents parse for answers. Required-field coverage, schema breadth, JSON-LD validity.

AEO Readiness

Question-style headings, concise answers, FAQPage usage — the patterns that let AI agents extract direct answers. Headings phrased as questions are the highest-leverage AEO signal.

Content Structure

H1 presence, heading hierarchy, content depth, paragraph length, list usage, and the semantic-HTML5 backbone. The structure AI agents walk to chunk content for retrieval.

SSR & AI Rendering

Schema and critical content present in raw HTML — not lazily JavaScript-injected. Most AI crawlers don't run JS. Detects when critical content depends on client-side JavaScript and is invisible in raw HTML.

Architecture for Traversal

Sitemap presence and depth, BreadcrumbList consistency, internal-link density — the navigable connections AI agents traverse to find related answers.

Payload Efficiency

How efficiently the page delivers content versus chrome — measured against the raw HTML text-length ratio.

Title & Identity

Page title quality, brand signals, Organization schema, canonical URLs, Open Graph basics, and meta-description sizing. The hygiene layer — most sites pass; the rare misses are quick fixes with outsized impact.

For Developers

Run scans from any MCP client

Add Obaron as an MCP server and scan any site without leaving your workflow. Works with Claude Code, Cursor, Continue, and any other MCP-compatible tool. Same scan, same score — inside the tools you already use.

Two tools available: aeo_scan — scan any domain. aeo_lookup — retrieve a previous result by ID.

Add to your MCP config

{
  "mcpServers": {
    "obaron-aeo": {
      "url": "https://api.obaron.ai/aeo/mcp"
    }
  }
}

FAQ

Frequently Asked Questions

The Lightning Scan is a free, instant check of any page's AI readiness. Enter your URL and in seconds you'll see a score out of 100, your top issues, and how you rate across 9 categories. No signup, no credit card.

Your AI Readiness Score maps to a tier: AI Invisible (0-40) means bots can't find you. AI Constrained (41-70) means standard SEO is working, but structural issues prevent AI agents from reading the full site. AI Optimized (71-90) means you're cite-ready with minor gaps. AI Native (91-100) means structurally clean across all 9 categories — AI consumers can read, cite, and act on the page without friction.

We check your site's readiness for AI engines like ChatGPT, Perplexity, Google AI Overviews, and Claude. Nine dimensions: AI Crawlability & Access, Documentation Patterns, Structured Data, AEO Readiness, Content Structure, SSR & AI Rendering, Architecture for Traversal, Payload Efficiency, and Title & Identity. The rubric is calibrated for developer documentation — API references, SDK guides, integration docs, and adjacent docs surfaces. Read the methodology →

No. We crawl your public pages just like an AI bot would. Nothing to install, no code to add. Just submit your domain. We respect robots.txt, AI-bot directives, and Crawl-delay headers — more on how Obaron respects bot controls.

Yes. No signup, no credit card. Scan as many sites as you want.

Yes. Obaron is available as an MCP server — works with any MCP-compatible client (Claude Code, Cursor, Continue, and more). Add one line to your config and scan any site without leaving your editor. See the setup.