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AI Search Citation-readiness
Score how well-positioned a page is to be cited by AI answer engines for a given query. We do NOT call ChatGPT, Perplexity, or Claude live. We score the agent-readable signals AgentSpeed can audit deterministically: query-term match, schema types matching query intent, authority signals, freshness, and llms.txt presence. Same input → same score, every time.
Tool
Examples
Click an example to run the tool with pre-filled inputs.
Why this matters for AI agents
Agents pick which sources to cite by reading the same signals search engines have always read, plus a few new ones. Pages that match the query in the title, name the answer in a heading, and declare structured authority typically beat pages that bury the answer in prose, no matter how good the prose is.
This tool is honest about what it is. It is not a live test of whether ChatGPT would cite you tomorrow. It is a deterministic score of the signals that agents read before deciding. Treat the score like Lighthouse: a useful proxy, not a guarantee.
Common fixes
Title does not match the queryRephrase the page title so the primary query terms appear verbatim in the first 60 characters. Agents weight title match more heavily than body match.
No query terms in any headingAdd an H2 or H3 that names the query intent. Agents skim by heading; an answer block is only as good as its label.
Wrong schema for the query intentPricing queries → Product / Offer. Informational queries → Article / FAQPage / HowTo. Use the structured-data validator to add the right schema.
No Organization or WebSite schemaAdd Organization + WebSite JSON-LD blocks site-wide. Without a structured publisher signal, agents fall back to text inference for attribution.
Old article with no datePublishedAdd `<meta property="article:published_time" content="…">` and a datePublished field on Article schema. Agents prefer recent content for informational queries.
Linked rubric checks
- structured_data.jsonld_present
- structured_data.type_coverage
markup.titleTitle presence and length, the primary citation match signal.
markup.title- llms_txt.present
FAQ
Is this a live ChatGPT / Perplexity call?
No. We do not call any model. The score is a deterministic readiness signal, so the same input always produces the same output. Predicting whether a specific model will cite a page on a specific day requires a live call that is rate-limited, costly, and non-deterministic. We deliberately do not pretend to do that here.
How is the score computed?
Six axes. (1) Query-term match in title: up to 20 points off if no match. (2) Query-term match in headings: up to 12 points off. (3) Body coverage: up to 15 off. (4) Schema types matching inferred query intent: up to 15 off. (5) Authority schema (Organization / WebSite): up to 8 off. (6) Freshness (informational queries only): up to 6 off. Plus a 6-point penalty if no /llms.txt on the host. Score clamped to 0–100.
How do you infer query intent?
Heuristic word presence: words like "buy", "price", "pricing", "subscribe" → transactional. Words like "what", "how", "why", "compare", "best", "guide" → informational. Words like "site", "login", "official" → navigational. Otherwise: unclear, treated as a navigational baseline.
What does "low / medium / high" likelihood mean?
A simple bucket on the score: 0–49 low, 50–74 medium, 75–100 high. The label is a convenience for sharing. The underlying deterministic score is what counts.
Will improving these signals guarantee citation?
No. Citation is also influenced by domain authority, content recency, the user's prompt phrasing, model temperature, and dozens of other variables we cannot audit. What this tool measures is the floor: the minimum signals an agent needs to consider you at all. Sites scoring high here are eligible; sites scoring low typically are not.
Run a full agent-readiness scan
Full scanScore the entire site against 30 agent-readiness checks across discoverability, readability, structured data, actionability, and performance.
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