How scoring works

Eight checks, all against public surfaces, all reproducible with plain HTTP requests. No invented standards: every check is traceable to a convention that real AI systems use in 2026.

Check	What & why	Points
A1	llms.txt — the llmstxt.org convention: a curated, plain-text entry point for AI readers. Substantive file with links required.	15
A2	AI-crawler policy — robots.txt rules for GPTBot, ClaudeBot, Claude-User, PerplexityBot, Google-Extended, CCBot. Open access scores full points; blanket blocks score zero and are labeled “closed by policy” (a stance, not a bug — but it is what it is).	15
A3	Content readable without a browser — a plain fetch of the homepage must yield real text, not a JavaScript wall or a bot challenge. Agents don't run your SPA.	25
A4	Structured data — valid schema.org JSON-LD so machines learn what the entity is.	15
A5	Sitemap — advertised in robots.txt or served at /sitemap.xml.	10
A6	Everyday agent tasks — pricing, support, docs, and legal reachable from the homepage; the links an assistant needs for the questions people actually ask.	20
B1	MCP server advertised at /.well-known/mcp.json — bonus; the emerging standard for callable sites.	+5
B2	OpenAPI document published — bonus; only relevant where an API exists.	+5

Grades

A ≥ 85 · B ≥ 70 · C ≥ 55 · D ≥ 40 · F < 40. Sites whose robots.txt blocks essentially all AI crawlers are labeled “Closed by policy” instead of graded — refusing agents is a legitimate choice, and we report it as one rather than pretending it's a defect.

Reproducibility

The evaluator is ~300 lines of dependency-free TypeScript in the open repo, runs weekly in public GitHub Actions, and commits raw results to the repository. Disagree with a check? Open an issue — the rubric is versioned in public.