MCP SERVER · OFFICIAL PROJECTTells you which URLs AI engines actually cite for a query — across Perplexity, Google AI Overviews, ChatGPT, Claude and Gemini — and what to fix so yours is one of them.
LABSCO SUMMARY
A URL cited by four of five engines is a different finding from one cited by a single engine, so start with the fan-out rather than a pinned engine. There is also a lot of work here that costs no API budget at all: the likelihood prediction, schema validation, sitemap sweeps and the crawler-access check run on public signals, which makes them the sensible first pass before you spend a key on anything.
A self-hosted citation-tracking server with no account and no central backend: you bring your own engine keys, and the results, panels and snapshots stay on your machine.
Node 20 or later, run with npx -y @automatelab/citation-intelligence. Nothing is hosted and there is no account — you set only the engine keys you have. PERPLEXITY_API_KEY, SERPAPI_KEY (100 requests a month free), GEMINI_API_KEY and BRAVE_API_KEY (2,000 a month free) all have free tiers; ANTHROPIC_API_KEY and OPENAI_API_KEY are paid. The project also lists BING_API_KEY for its bing_serp engine, but Microsoft retired the Bing Search APIs in August 2025, so that engine no longer works. The prediction, Wikipedia, sitemap, schema and crawler-access tools need no key at all. Caching is on by default via CITATION_CACHE_TTL_DAYS (7 days) and CITATION_AI_OVERVIEW_TTL_DAYS (1 day), and CITATION_CONFIG_DIR moves the config, panels and snapshots off the default ~/.config/citation-intelligence.
One command — npx -y @automatelab/citation-intelligence
USE CASES
Check the domain across a cluster of queries, then read back from cache what it has been cited for.
Fan the query across engines for consensus, then pull the snippet that explains each citation.
Score it from public signals, then validate the structured data the score flagged.
Parse robots.txt and do a live fetch as each bot's user agent to catch both kinds of block.
Save the query watchlist, run it on a schedule, then read the rate over the stored snapshots.
Find the domains every engine treats as authoritative for the query, then compare your URL against them.
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
audit_crawler_accessVerify 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.
audit_llms_txtGenerate 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 <title> per URL (slower, richer output).
audit_schemaDeep 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.
audit_sitemapFetch 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.
audit_sitemap_mapCross-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.
audit_structured_dataSuggest 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.
citations_checkReturns the URLs an AI engine cites for a query — Perplexity, Claude, ChatGPT, Gemini or Google AI Mode — or, with brave_serp, a page's plain web-search rank. Needs at least one engine key and uses the first one it finds. The project's bing_serp engine calls the Bing Search API, which Microsoft retired in August 2025, so it no longer returns results.
citations_evidenceExtracts the cited snippet from the AI engine's raw answer for each citation. Calls citations_check, then for each returned URL finds its first mention in raw_answer and returns a context window plus the nearest quoted span or containing sentence — why an engine cited a URL, not merely that it did. Returns 'not found' for engines that expose no raw_answer, such as Brave.
citations_freshnessScore 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.
citations_predictScore 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.
citations_provenanceFan 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.
citations_trendReport 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 <config>/snapshots/<panel>/. 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.
competitors_canonical_setFan 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.
competitors_compareRun 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.
competitors_competeEnd-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.
domain_am_i_citedCheck 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.
domain_cited_forList 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.
domain_cited_for_diffDiff 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.
panel_runRun 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 <config>/snapshots/<panel>/<iso>.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.
panel_trackSave, 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 <config>/panels/<name>.json.
report_visibilityTurnkey 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.
signals_ai_overviewCheck whether Google shows an AI Overview for a query, and which URLs it cites. Uses SerpAPI (free tier: 100/month). Set SERPAPI_KEY.
signals_answer_boxLocates where each cited URL appears in the AI engine's raw answer text. Calls citations_check, finds the first mention of each citation's URL or hostname in raw_answer, and bins it by character position into early, middle or late thirds, showing whether your URL is cited up front or buried near the end. Returns 'unknown' for engines that expose no raw_answer, such as Brave.
signals_bing_gapJoin 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).
signals_gsc_gapJoin 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).
signals_wikipediaList 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.
BEFORE YOU INSTALL
26 tools when we connected. signals_bing_gap registers but is missing from the documentation's tables, which name 25. The five audit_ prompts, such as audit_gap_analysis, and the five citation:// resources are not tools.
npx on PATH
All 4 documented clients run the server on your machine.
CONTEXT COST
Rated by the size of the 26 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
npx -y @automatelab/citation-intelligenceSet only the keys you have and delete the rest — the prediction, sitemap, schema and crawler-access tools need none. BRAVE_API_KEY goes in the same env block if you use that engine. Skip BING_API_KEY: Microsoft retired the Bing Search APIs in August 2025, so the bing_serp engine no longer works.
{
"mcpServers": {
"citation-intelligence": {
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
],
"env": {
"PERPLEXITY_API_KEY": "<YOUR_PERPLEXITY_API_KEY>",
"SERPAPI_KEY": "<YOUR_SERPAPI_KEY>",
"ANTHROPIC_API_KEY": "<YOUR_ANTHROPIC_API_KEY>",
"OPENAI_API_KEY": "<YOUR_OPENAI_API_KEY>",
"GEMINI_API_KEY": "<YOUR_GEMINI_API_KEY>"
}
}
}
}MCP servers
