Return URLs cited by an AI engine (Perplexity, Claude, ChatGPT, Gemini, or Bing) for a query. Use this when an agent or user wants to see what sources an AI search engine grounds answers on. Requires at least one engine API key; auto-picks the first available.

Check whether a domain is cited by an AI engine across a cluster of queries. Returns per-query presence, rank, and a citation-rate summary. Use to measure visibility for a brand, product, or content site in AI search.

Check whether Google shows an AI Overview for a query, and which URLs it cites. Uses SerpAPI (free tier: 100/month). Set SERPAPI_KEY.

List queries that the given domain has been cited for, served from the local cache. Build up a corpus by calling check_citations or am_i_cited first; cited_for queries it without spending API budget.

Score citation likelihood for a URL from public signals (Wikipedia link presence, schema.org markup, /llms.txt, GitHub and Reddit references, canonical hygiene, HTTPS). No LLM fired - all heuristic. Returns 0-100 score, grade, signal breakdown, and ranked fixes.

Save, load, or list named query panels. A panel is a persisted set of queries you want to monitor over time (e.g. editorial-watchlist). Use action=save with queries[] to create, action=load to read, action=list to enumerate. Panels live under /panels/.json.

Run a saved panel through am_i_cited and append a timestamped snapshot. Side effects: makes external API calls to the configured AI engine (costs API quota); writes one snapshot file to disk at /snapshots//.json. Requires at least one engine API key (same as am_i_cited). Returns per-query citation presence and a citation_rate summary for the run. Use panel_track to create a panel first; use citations_trend to read the accumulated trend after multiple runs.

Turnkey AI visibility report for a domain across a query set. Composes check_citations over every query (or a saved panel) and returns the metrics AI-visibility trackers sell as a dashboard, in one call: mention frequency (citation_rate), share_of_voice vs competitors, average rank when cited, and brand sentiment from the answer text. Side effects: one check_citations call per query (costs API quota for uncached queries; cached queries are free). Returns structured summary + top_domains + per_query, plus a rendered Markdown report (include_markdown=true) suitable for a public page. Provide queries[] or a panel name. Same engine selection as check_citations.

Report citation rate over time for a panel from stored snapshots. Read-only; cache-only — makes no API calls to any AI engine and costs no API quota. Reads snapshot files from /snapshots//. Returns: snapshots[] (one entry per panel_run invocation, each with timestamp and citation_rate), plus per-query deltas (gained/lost/unchanged) comparing first vs last snapshot. Returns an empty series when no snapshots exist yet. No auth required. No rate limits. Use panel_run to accumulate snapshots first; use since to restrict the time window.

Run predict_citation on 2-10 URLs and return a side-by-side signal table plus a list of signals where the URLs diverge. Use to compare your URL to top-cited competitors for the same query.

List Wikipedia articles that reference the given domain. Read-only. One HTTPS GET to the Wikipedia API (en.wikipedia.org/w/api.php?action=query&list=exturlusage). No auth required; no API keys; no rate limits beyond Wikipedia's public API fair-use policy (~1 request/second). Returns article titles and URLs. Wikipedia backlinks are the highest-lift signal for LLM training corpora — a domain cited from Wikipedia is far more likely to appear in AI training data and citation pools. Use lang to query non-English Wikipedias.

Fetch a sitemap.xml (or sitemap index) and run predict_citation on every URL. Returns results sorted worst-score-first. Surfaces systemic issues across a whole site in one pass. Zero engine keys needed.

End-to-end competitive snapshot for a single query. Calls check_citations to get the cited URLs, then runs compare_domains on your_url vs the top cited competitors. Returns your score, the average competitor score, and the gap.

Score how recent the pages cited for a query are. Calls check_citations, then collects dateModified for each cited URL, returns a 0-100 recency_score (halflife=365d) plus per-URL freshness bucket (fresh/current/stale/ancient/unknown). Surfaces queries where AI cites old content - opportunity to ship fresher.

Diff cited_for between two time windows for a domain. Returns queries gained (cited now, not before baseline_until) and queries lost (cited before, not since current_since). Cache-only, no API spend. Use to track citation drift over time after publishing or migrating content.

Join Google Search Console performance with am_i_cited per query. Surfaces queries where the domain ranks well in Google but is not cited in AI - the closest editorial wins. Requires GCP service account creds (credentials_path or GOOGLE_APPLICATION_CREDENTIALS env).

Join Bing Webmaster Tools query stats with am_i_cited per query. Surfaces queries where the domain ranks well in Bing but is not cited in AI - the closest editorial wins. Bing's index backs Copilot/ChatGPT/Perplexity grounding, so a Bing rank gap is an LLM-citation gap. Requires BING_WEBMASTER_API_KEY (Bing Webmaster Tools -> Settings -> API Access).

Deep schema.org validation for a URL. Parses every JSON-LD block and microdata node, checks required fields per @type (Article needs headline+author+datePublished, FAQPage needs mainEntity, HowTo needs step, etc.), and flags missing fields and malformed JSON-LD. Returns issues list and a valid/invalid verdict. Use to fix structured-data bugs that predict_citation flags but can't explain.

Generate an llms.txt file (https://llmstxt.org spec) from a sitemap. Parses sitemap.xml + nested indexes, groups URLs by top-level path, and emits a Markdown document with H1+description+sectioned link lists. Set fetch_titles=true to pull per URL (slower, richer output).

Locate where each cited URL appears in the AI's raw answer text. Calls check_citations, finds the first mention of each citation's URL (or hostname) in raw_answer, and bins by char position into early/middle/late thirds. Surfaces whether your URL is cited up-front or buried near the end. Returns 'unknown' for engines without raw_answer (Bing, Brave).

Fan a query out across multiple AI engines and report per-URL cross-engine consensus. Returns each unique cited URL with the list of engines that cited it, plus a consensus_urls list (URLs cited by ALL engines). High engine_count = strong cross-engine citation signal; engine_count=1 = engine-specific.

Extract the cited snippet from the AI engine's raw answer for each citation. Calls check_citations, then for each returned URL finds the first mention in raw_answer and returns a context window plus the nearest quoted span or containing sentence. Use to see why an engine cited a URL, not just that it did. Returns 'not found' for engines without raw_answer (Bing, Brave).

Verify that major AI crawlers (GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, CCBot, Google-Extended, Applebot-Extended, Bytespider, Meta-ExternalAgent, plus real-time fetch UAs) can fetch a URL. Parses robots.txt and does a live GET with each bot's User-Agent. Surfaces robots.txt blocks AND UA-based gating that breaks AI citation.

Cross-reference a sitemap with the citation cache. For each sitemap URL, reports whether it appears in cached citations (and how many queries/engines cited it). Inverse of audit_sitemap: not 'how citable is each URL', but 'has each URL actually been cited yet'. Cache must be primed via check_citations or run_panel first.

Fan a query across engines and aggregate citations by registered domain (not URL). Returns top competitor domains ranked by cross-engine consensus, with per-engine breakdown and top URLs per domain. Use to identify the canonical competitor set for a query - the domains every engine treats as authoritative.

Suggest missing JSON-LD additions for a URL. Fetches the page, detects existing schema types, and returns ready-to-paste templates for types that are missing but signalled by page content (BlogPosting from og:type=article or bylines, FAQPage from Q&A pairs, HowTo from numbered steps, BreadcrumbList from nested paths, Organization on homepages). Templates are pre-filled from page metadata where possible; fields marked FILL: require manual completion.