Context7
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Beschreibung vom Entwickler (englisches Original)
Check what Perplexity, Claude, ChatGPT, Gemini, and Google AI Overviews cite for any query.
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Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"citation-intelligence": {
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add citation-intelligence -- npx -y @automatelab/citation-intelligenceIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"citation-intelligence": {
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"citation-intelligence": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
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Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
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Dieses Repo haben wir noch nicht von GitHub gelesen
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Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.12.1) kam vor 24 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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A free, self-hosted MCP server that tells your agent what LLMs cite - across Perplexity, Google AI Overviews, ChatGPT, Claude, Gemini, and Bing.
An MCP server for agents and developers who need to know which URLs get cited by AI search engines for any query. Install once, query from any MCP-compatible client (Claude Desktop, Cursor, Claude Code, Continue, Cline, n8n, LangGraph). Self-hosted, no account, no centralized backend. Bring your own API keys; nothing is stored on a remote server.
Install this if you're:
Do NOT install this if you want:
The AI citation tracking market is dominated by VC-funded dashboards starting at $295/mo. None ships MCP-first. If you're an agent or developer who wants citation data piped directly into your workflow - not into a SaaS login - there isn't a tool for you. This is that tool.
Tools are grouped into seven namespaces: citations_*, domain_*, signals_*, panel_*, report_*, competitors_*, audit_*. The prefix is the question category; the suffix is the action. Wire names use underscores (not dots) so Anthropic-API-based MCP clients (Claude Desktop, Claude Code) can forward the tool list without HTTP 400.
Start with `citations_provenance` or `domain_am_i_cited`. Single-engine results (citations_check with a pinned engine) are directional; multi-engine consensus is the honest signal. A URL cited by 4 of 5 engines is a very different finding than one cited by 1.
citations_* — query-level: who cites what, with what evidence| Tool | Purpose |
|---|---|
citations_provenance | Recommended first tool. Fan a query across engines; per-URL cross-engine consensus matrix. Returns interpretation_note per engine. |
citations_check | URLs cited by Perplexity / Claude / ChatGPT / Gemini / Google AI Mode for a query; or web rank via bing_serp / brave_serp |
citations_evidence | Extract the cited snippet from raw_answer for each citation (why, not just that) |
citations_predict | Citation likelihood from public signals - no LLM fired |
citations_trend | Time-series report of citation rate + per-query gained/lost deltas |
citations_freshness | Recency score (halflife=365d) for the pages an engine cites |
domain_* — domain-level: am I cited, what for| Tool | Purpose |
|---|---|
domain_am_i_cited | Domain citation check. With engine=auto (default): fans across all available LLM engines, returns per-engine breakdown + cross-engine consensus. Pin engine= to reduce cost. |
domain_cited_for | Queries the domain has been cited for, from local cache |
domain_cited_for_diff | Diff of domain_cited_for between two time windows for a domain |
signals_* — external signals: AI Overview, Wikipedia, GSC, answer-box position| Tool | Purpose |
|---|---|
signals_ai_overview | Google AI Overview presence + cited sources |
signals_wikipedia | List Wikipedia articles referencing a domain (zero keys) |
signals_gsc_gap | Join Google Search Console performance with AI citation status |
signals_answer_box | Bin each citation's first mention in raw_answer into early/middle/late thirds |
panel_* — saved query panels (editorial watchlists)| Tool | Purpose |
|---|---|
panel_track | Save / load / list named query panels (editorial watchlists) |
panel_run | Run a panel through domain_am_i_cited and snapshot to disk |
report_* — turnkey reporting artifacts| Tool | Purpose |
|---|---|
report_visibility | One-call AI visibility report over a query set (or panel): citation rate (mention frequency), share of voice vs competitors, average rank, and brand sentiment. Returns structured data + a Markdown artifact for a public page. |
competitors_* — competitive landscape per query| Tool | Purpose |
|---|---|
competitors_canonical_set | Top cited domains per query, aggregated across engines |
competitors_compete | End-to-end competitive snapshot: your URL vs top cited competitors |
competitors_compare | Side-by-side citations_predict across 2-10 URLs |
audit_* — fixable on-page / on-site checks| Tool | Purpose |
|---|---|
audit_schema | Deep schema.org validation - required fields per @type, malformed JSON-LD |
audit_structured_data | Repair-oriented schema.org diagnostics + suggested patches |
audit_crawler_access | Verify GPTBot / ClaudeBot / PerplexityBot / CCBot / Google-Extended etc. can fetch a URL |
audit_sitemap | Bulk citations_predict across every URL in a sitemap, worst-first |
audit_sitemap_map | Cross-reference sitemap URLs with cached citations (inverse of audit_sitemap) |
audit_llms_txt | Generate an llms.txt (https://llmstxt.org) from a sitemap |
Server-side prompt templates the client can offer end users (call via the MCP prompt list):
audit_citation_readiness(url) - chains citations_predict + audit_schemaaudit_competitor_snapshot(query, your_url?) - chains competitors_canonical_set + competitors_competeaudit_crawler_checkup(url) - runs audit_crawler_access and writes a remediation listaudit_gap_analysis(domain, days?) - drives signals_gsc_gap and suggests next movesaudit_sitemap_coverage(sitemap_url) - runs audit_sitemap_map and recommends prioritiesCache views the client can read or subscribe to (no tool call required):
citation://cache/summary - entry counts by type/engine, unique queries/URLs, oldest/newestcitation://panels - saved panels + per-panel snapshot countscitation://docs/llms-txt - llms.txt primer (markdown)citation://docs/ai-crawlers - AI crawlers cheatsheet (markdown)citation://domain/{domain}/cited-for - dynamic template: citations for {domain}Every response includes a surface field that tells you exactly how the data was collected. Understanding this is important before drawing conclusions.
| Surface | Engines | What it means |
|---|---|---|
consumer_scrape | perplexity, google_ai_mode | Proxied through a real consumer-facing AI search product. Closest to what your users see. |
api_proxy | claude, openai, gemini | API call to a search-enabled LLM. May differ from consumer product behavior — different model versions, no UI-level ranking logic, no personalization. Use as a directional proxy, not as ground truth. |
web_rank | bing_serp, brave_serp | Traditional web search rank (not LLM citation). Measures whether a URL appears in SERP results, not whether an LLM cites it. |
static_signal | citations_predict, signals_wikipedia | Offline signal computed from public data. No live LLM query. |
`perplexity` (consumer_scrape) — Sonar Pro via the Perplexity API with a consumer-equivalent system prompt. Reasonably close to Perplexity.ai. Citations come from search_results in the response; the citations fallback contains URL-only entries without title.
`claude` (api_proxy) — Claude Sonnet via the Anthropic Messages API with web_search tool enabled. The consumer Claude.ai product uses different routing and ranking logic. Citation behavior can differ, especially for recent/time-sensitive queries.
`openai` (api_proxy) — gpt-4o + the web_search_preview tool via the OpenAI Responses API. Replaces the deprecated gpt-4o-search-preview alias OpenAI retired; base gpt-4o plus the tool is the supported path.
`gemini` (api_proxy) — Gemini 2.5 Pro via the Generative Language API with google_search grounding. Consumer Gemini uses the same grounding index but different re-ranking. Results are directional.
`google_ai_mode` (consumer_scrape) — Google AI Mode results via SerpAPI. Closest to what users see in Google Search. Requires SERPAPI_KEY.
`bing_serp` / `brave_serp` (web_rank) — Traditional SERP rank. Does NOT measure LLM citations. Use citations_check with these engines to compare organic web rank against LLM citation rank. domain_am_i_cited refuses these engines — it only measures LLM behavior.
The proxy nature of api_proxy engines is a feature, not a bug: it lets you run citation checks without consuming expensive consumer-product quota. Just don't report API-proxy numbers as "ChatGPT cites you" without the caveat.
Every tool response includes an interpretation_note field that summarizes the fidelity in one sentence. Full per-engine fidelity ratings: docs/surface-fidelity.md.
npx -y @automatelab/citation-intelligence
Requires Node 20 or later.
Add to %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):
{
"mcpServers": { "citation-intelligence": { "command": "npx", "args": ["-y", "@automatelab/citation-intelligence"], "env": { "PERPLEXITY_API_KEY": "pplx-...", "SERPAPI_KEY": "...", "ANTHROPIC_API_KEY": "sk-ant-...", "OPENAI_API_KEY": "sk-...", "GEMINI_API_KEY": "..." } } } }
Set only the keys you have. Any MCP client that supports stdio transport works - same command / args pattern.
~/.config/citation-intelligence/cache.json. Repeated queries hit cache, not API. Default TTL: 7 days.~/.config/citation-intelligence/cache.json. Delete it any time.| Var | Purpose | Free tier? |
|---|---|---|
PERPLEXITY_API_KEY | citations_check (perplexity — consumer_scrape) | Yes |
| SERPAPI_KEY | signals_ai_overview + citations_check (google_ai_mode — consumer_scrape)
Nur der Anfang wird gezeigt; den Rest findest du im Repo