Agent Ready is a free agent-readability checker for websites. Enter any public URL and it returns a 0โ100 score for how well AI agents, crawlers, and LLMs can understand and act on your site - plus a plain-English fix for every issue it finds.
It validates against the standards that govern the agentic web: โข Vercel Agent Readability Spec - semantic HTML, metadata, structured data, content clarity โข llmstxt.org - llms.txt and llms-full.txt discovery files โข Agent-protocol manifests - MCP server cards, A2A, agents.json, agent-permissions.json, NLWeb, x402, and more
Results are grouped into Site, Page, llms.txt, and Protocol checks, each with a clear pass/fail and a remediation step, so developers can ship changes and re-scan immediately. A corpus benchmark shows how your site ranks against every other site scanned, and every scan gives you a shareable result URL and an embeddable SVG score badge.
Agent Ready is available wherever you work: web app, REST API, MCP server (Claude Desktop, Cursor, Cline, Goose, Continue), CLI (npm: agent-ready-scanner), client SDKs (npm + PyPI: agent-ready-client), and a browser extension for Chrome, Edge, and Firefox.
Freemium: scan free with no signup (3 scans/30 days anonymous, 10 signed in, 25 pages each). Pro ($19/month) adds deeper scans, history, monitoring, and API/MCP access. Built for developers, technical founders, and DevRel teams.
Agent-readiness score
0โ100 score per scan with rating bands
Standards validated
Vercel Agent Readability Spec, llmstxt.org, agent-protocol manifests
Check categories
Site, Page, llms.txt, and Protocol checks with per-issue fixes
Pages per scan
Up to 25 (Free), up to 250 (Pro)
Corpus benchmark
Percentile ranking against every site scanned
Shareable Results
Yes - high-entropy share URL per scan
Score badge
Embeddable SVG badge
Access surfaces
Web, REST API, MCP server, CLI, client SDKs, browser extension
No signup to scan
Yes - anonymous scanning, no API key
Most agent-readiness tools check a shallow list of well-known paths and produce inconsistent or false-positive results. Agent Ready validates against the actual published specs in depth, groups findings into Site / Page / llms.txt / Protocol layers, and gives a concrete remediation step for each. It scans free with no signup, shows how your site ranks against the whole corpus, and meets you wherever you work - API, MCP, CLI, SDK, or browser extension - not just a single web form.
Developers, technical founders, and DevRel teams preparing their websites for AI search and autonomous agents. Anyone who needs their site to be discoverable and usable by LLMs, AI assistants, and agentic crawlers.
Agent Ready is the only readiness checker grounded in the full set of agentic-web standards at once - the Vercel Agent Readability Spec, the llmstxt.org standard, and the major agent-protocol manifests (MCP, A2A, agents.json, NLWeb, x402). Independent scanners score the same site anywhere from 46 to 96 because they disagree on what to check; Agent Ready's value is spec fidelity and depth of protocol validation, with a plain-English fix for every issue. It's also available on every surface developers use - web, REST API, MCP, CLI, SDKs, and a browser extension.
As AI assistants and autonomous agents became a primary way people find products and information, websites built only for human visitors started getting left behind - and there was no precise, standards-grounded way to know how "agent-ready" a site actually was. Agent Ready was built to fill that gap: a rigorous, spec-faithful score plus actionable fixes, so teams can prepare for the agentic web with confidence instead of guesswork.
Next.js (App Router) and TypeScript, Tailwind CSS with shadcn/ui, Postgres for scan storage, deployed on Vercel. Standards/protocols implemented include the Vercel Agent Readability Spec, llmstxt.org, Model Context Protocol (MCP), A2A, agents.json, NLWeb, and x402.
Agent-Ready.dev appears to be a niche developer-focused resource centered on preparing websites, APIs, or tools for AI agent interaction, but without verified public data on performance, reviews, or company backing, it's difficult to fully confirm its quality or reliability.
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