Test whether an AI engine would cite this page
score_test_citationSimulate whether an AI engine would cite your page for a specific query. Get a cite/no-cite verdict with verbatim excerpt and ranked improvements.
Instructions
Simulate would an AI engine cite this page for this query?. The host LLM role-plays the chosen engine (chatgpt / claude / perplexity / google_ai_overviews / any), reads the page content, and returns a cite/no-cite verdict with the verbatim excerpt it would surface plus ranked improvements.
Read-only when given url (one HTTP GET). Zero network when given text.
Primary path uses MCP sampling. If the host doesn't support sampling, falls back to a deterministic heuristic derived from score_citation_worthiness (overall_score + per-engine subscore must both clear thresholds). The result includes mode: "sampling" | "static_heuristic" so callers can tell which path ran.
When to use: pre-publish gut-check for a specific query, or auditing whether existing content earns citation surface. Distinct from score_citation_worthiness (deterministic 0-100 score) and audit_page (whole-page rubric); this returns a binary cite/no-cite verdict tied to one query.
Either url or text must be provided. target_query is required.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Public URL to fetch and test. Either this OR `text` is required. | |
| text | No | Raw text/HTML to test directly. Either this OR `url` is required. | |
| engine | No | Which engine to simulate. `any` (default) uses a generic AI-search persona. Specific engines tune the cite criteria (e.g. perplexity favors statistic-dense excerpts; google_ai_overviews favors schema + freshness). | any |
| target_query | Yes | The user query the engine is answering. Required. Example: `how to add JSON-LD to a Next.js app`. | |
| respect_robots | No | If true (default), respect robots.txt when fetching `url`. Ignored when `text` is used. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | Which code path produced the result: MCP sampling (host LLM) or deterministic heuristic fallback. | |
| engine | Yes | Which engine persona produced the verdict. | |
| reasoning | Yes | Plain-language explanation of the verdict. | |
| confidence | Yes | How likely a real engine is to surface this page (0-100). | |
| would_cite | Yes | Binary verdict: would the simulated engine cite this page for the query? | |
| improvements | Yes | Ranked, specific edits to improve citation probability. | |
| target_query | Yes | The query the engine simulation answered. | |
| blocking_issues | Yes | Concrete reasons the page is (or is not) citable. Empty if would_cite=true and excerpt is clearly attributable. | |
| citation_excerpt | Yes | If would_cite=true, the 20-60 word verbatim excerpt the engine would surface; otherwise null. |