AI Site Scorer is a public readiness scanner. Give it any domain and it runs real HTTP probes — as GPTBot, ClaudeBot, PerplexityBot and other agents — then checks the emerging standards that decide whether AI systems can find, read and cite your site: llms.txt, robots Content Signals, structured data, MCP and API catalogs. You get a graded scorecard, the exact failing checks, and how to fix them.
The web is shifting from ranked links to AI-generated answers — and those answers are assembled by machines that crawl, parse and judge your pages on their own terms. If an agent can’t reach, read or trust your site, you never appear in the reply a user sees, and you never find out why. This check shows exactly what today’s AI systems can and cannot do with your site — before a competitor’s page becomes the default answer instead of yours.
The next web is not just read by AI, it is acted on by it. Emerging standards like x402 (HTTP 402 Payment Required, with machine-readable terms) let autonomous agents complete a purchase end to end — discover a product, evaluate the seller and pay — without a human ever opening the page. Protocols such as MCP, agent cards and API catalogs are how an agent decides you are worth transacting with in the first place. Your business will depend directly on how AI systems see, trust and act on you.
msu.edu scores 115/194 (59%) for AI agent readiness, grade D. 15 of 37 checks fail across discoverability, content, bot access, policy and capabilities.
Scanned 2026-07-26. Figures update on re-scan.
