How the Slurp Score works

Updated July 2026, engine version 0.1

The Slurp Score is a 0-100 measure of how ready a page is to be found, read and cited by AI search engines: ChatGPT, Gemini, Google AI Overviews and AI Mode, and Claude. It is built from 60+ deterministic checks. Every check tells you why it matters and exactly how to fix it. This page is the complete method, there is nothing else in the score.

What the score measures, and what it doesn't

The Slurp Score measures readiness: whether an AI search engine can reach your page, read it, trust it, and cite it. It does not measure whether ChatGPT cited you this morning. Live AI citations swing from day to day and query to query, and any tool that hands you a single fixed "you appeared" number is smoothing over that. GetSlurp scores the inputs you control, with checks that return the same result every time you run them. Fix the inputs and you improve your odds across every engine at once.

Three tiers of checks

Gates. Pass or fail conditions that cap the score at 49 for the affected engine. Example: robots.txt blocks PerplexityBot. A page that no engine can fetch cannot be cited, so no amount of on-page polish should produce a high score. Gates are why a GetSlurp 90 means something.

Graded checks. Weighted checks across the categories below. Eleven of the twelve carry weight today; the twelfth, agent readiness, is reported but not yet scored. Each check passes, warns or fails and feeds its category score. Checks that do not apply to a page type (a homepage is not an article) are excluded rather than counted against it.

Informational. Reported, never scored. llms.txt, WebMCP readiness, and training-bot blocks live here. If the evidence says a factor does not affect AI visibility, it gets zero weight - visibly.

Per-engine scores

ChatGPT, Gemini, Google and Claude pull from different crawlers against different indexes, and published citation studies show their source pools barely overlap. So GetSlurp reports one score per engine. Today they diverge when engine-specific gates fire; engine-specific weighting is being calibrated and will widen the differences.

The 12 categories

Category	Weight	What's checked	Key evidence
AI crawler access	15%	robots.txt rules for every AI bot (search, training and live-fetch tiers), noindex, snippet controls, sitemap, HTTP behavior	Official bot documentation from OpenAI, Anthropic, Perplexity and Google
Renderability	15%	Content present in raw HTML, app-shell detection, framework SSR verdict, text ratio, DOM size	Vercel and MERJ crawler study: no major AI crawler executes JavaScript
Answer-readiness	15%	Lead paragraphs, question headings, section length, lists and tables, summaries, definitions, content position	Citation-position studies; passage-level retrieval in Google AI Mode
Citability and evidence	12%	Statistics, quotations, outbound citations, claim-evidence pairing, keyword stuffing, readability	Princeton GEO paper (KDD 2024): measured lifts of +31.5% (statistics), +40.7% (quotations), +29.1% (citing sources)
Entity and E-E-A-T	12%	Author markup, Organization schema, sameAs profiles, trust pages, brand naming consistency	E-E-A-T visibility studies; earned-media bias findings in AI search
Freshness	8%	Machine-readable dates, recency, year signals, stale-signal contradictions	Roughly half of AI-cited content is under 13 weeks old
Structured data	8%	Valid JSON-LD, content-specific types, schema-to-page consistency, FAQ, breadcrumbs	Bing confirms schema use; evidence for direct citation impact is mixed, and we say so
Semantic HTML	6%	Single H1, heading order, landmarks, lang, link text quality	HTML specification; machine legibility for parsers and browsing agents
Meta and discoverability	5%	Title, description, canonical correctness, Open Graph, URL clarity	Wrong canonicals silently redirect citation credit
Internal linking	4%	Internal link count, breadcrumbs, anchor quality	Crawl discovery mechanics
Multimodal	3%	Alt text coverage, video transcripts, media schema	Text alternatives are the only path into text-based retrieval
AI agent readiness	info	WebMCP signals, MCP references, form operability for browsing agents	W3C WebMCP effort; ChatGPT-User and Claude-User already operate pages live

Reported, not scored

Some things belong in your report without touching your score. Keeping them at zero weight is what keeps the Slurp Score meaningful.

llms.txt. The major engines don't currently read it. GetSlurp detects it and reports it, and it starts counting when engines start using it.
WebMCP and agent standards. AI agents that operate sites are the next wave, and WebMCP is the standard to watch. GetSlurp already checks your agent readiness. The weight goes above zero the day engines ship support, and early adopters will already be passing.
Training-bot blocks. Blocking GPTBot or Google-Extended is a content-rights choice that does not remove you from AI answers. We report it neutrally and never penalize it.