Agent Ready
No sign-up required — scan instantly

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.

Last updated

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

12 checks covering MCP, A2A, agents.json, UCP, x402, and NLWeb

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 — 12 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.