AI Agent-Readiness Checker

What this tool checks

Agent-readiness measures whether AI agents and answer engines — ChatGPT, Perplexity, Google AI Overviews — can actually consume your pages, not merely find them. This tool returns a free 0–100 consumability score built from the offline signals that decide it: an llms.txt index, per-page markdown alternates, JSON-LD schema, named authors, and sameAs entity grounding. It matters because an engine that cannot cleanly parse and attribute your content will summarize a competitor instead of citing you.

What it looks at

llms.txt index

A root-level file (per llmstxt.org) listing your key URLs in a form AI agents and IDE assistants route on; without it, crawlers guess at your site's structure.

llms-full.txt companion

A single file bundling your full page bodies so an engine can ingest the whole site in one fetch instead of crawling it page by page.

markdown alternates

A per-page rel=alternate text/markdown link that serves clean markdown, which RAG pipelines and answer engines parse far more reliably than rendered HTML.

JSON-LD schema coverage

The share of pages shipping structured data; agents prefer machine-readable schema to infer what a page is about and pull exact facts.

named author byline

A real, attributable author on each page; answer engines weight attributable expertise (E-E-A-T) when deciding what to trust and cite.

sameAs entity grounding

sameAs links in your schema that tie your brand or author to knowledge-graph entities like Wikidata or LinkedIn, so an engine can resolve who you are with confidence.

How to use it

1. Enter your site URL to run the free 0–100 agent-readiness scan.
2. Review the flagged gaps — a missing llms.txt, low schema coverage, unattributed pages — ranked by how many points each one costs.
3. Fix the highest-weighted gaps first (schema and llms.txt move the score most), then re-run to confirm it climbs.

Frequently asked questions