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Is your site ready for AI agents?

Score any website against the Vercel Agent Readability Spec and llmstxt.org standard. Get actionable fixes in seconds.

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Readability spec

15 site-wide + 23 per-page checks from the Vercel Agent Readability Spec

llms.txt

10 checks against the llmstxt.org specification for LLM-friendly content

Agent protocols

15 checks covering MCP, A2A, agents.json, UCP, x402, NLWeb, API Catalog, Web Bot Auth, and Agent Skills Discovery

Fix guidance

Every failing check includes a clear, actionable how-to-fix explanation

Building with Agent Ready? Developer documentation — REST API, MCP server, OpenAPI spec, and installable skills.

What is agent readability?

Agent readability is how easily AI agents — ChatGPT, Claude, Perplexity, Google Gemini, coding assistants, MCP clients — can discover, parse, and act on a website. It spans three surfaces: discovery files (llms.txt, robots.txt, sitemaps), structural signals (semantic headings, canonical links, structured data, markdown mirrors), and protocol manifests (MCP Server Cards, A2A Agent Cards, agents.json, agent-permissions.json).

Why does AI agent readability matter for SEO?

AI agents crawl what loads cleanly and cite what parses correctly. The incentives are sharp: a July 2025 Pew Research study found users who encounter a Google AI Overview click on a source link only about 8% of the time — roughly half the rate of searches without an AI summary. Princeton’s GEO study (KDD 2024) measured that adding source citations to a page lifted its inclusion in AI answers by roughly 40%, with statistics and quotations close behind. Sites that score well get summarised accurately and referred qualified traffic; sites that score poorly get paraphrased (badly) or skipped entirely. Unlike traditional SEO, you don’t need to rank on page 1 — structured, citable content gets pulled even when organic rank is low.

What does the agent readability scanner check?

  • Vercel Agent Readability Spec — 15 site-wide checks (llms.txt, robots.txt, sitemap.xml, sitemap.md, AGENTS.md, HTTPS, OpenAPI) plus 23 per-page checks (meta tags, JSON-LD, headings, markdown mirrors, content negotiation, code-block language tags, JS-rendering dependency).
  • llmstxt.org — 10 checks against the llms.txt format (H1 present, blockquote summary, H2 sections, link format, content-type, llms-full.txt).
  • Agent protocols — 15 checks covering MCP Server Cards (SEP-1649 / RFC 9728 OAuth Protected Resource metadata), A2A Agent Cards (a2a.proto v1.0.0), Wildcard agents.json, agent-permissions.json, UCP (Universal Commerce Protocol), x402 (HTTP 402 Payment Required), and NLWeb (natural-language /ask endpoint).

How is the agent readability score calculated?

score = round((passed checks / total checks) × 100). The denominator compounds: 15 site-wide + (23 per-page × number of pages scanned). A systemic issue like a missing canonical link on every page compounds significantly. Ratings: 90-100 Excellent, 70-89 Good, 50-69 Fair, 0-49 Needs Improvement.

Why choose Agent Ready over an SEO scanner or manual audit?

Agent Ready is built specifically for AI-agent readability — not a human-search SEO tool with an “AI” tab bolted on. It is the only scanner that validates llms.txt, the full Vercel Agent Readability Spec, and every agent-protocol manifest (MCP, A2A, agents.json, agent-permissions.json, UCP, x402, NLWeb) in a single pass.

  • vs general SEO crawlers (Lighthouse, Screaming Frog) — they optimise pages for human search engines and never check the agent-protocol surfaces AI agents read.
  • vs manual audits — all 68 checks run in seconds, every deploy, instead of hand-verifying five specs by hand.
  • vs single-spec llms.txt validators — those lint one file; Agent Ready covers the other ~50 conditions too, with a plain-English fix for each failure.

See the full breakdown: Agent Ready vs the alternatives.