Citation Intelligence MCP

A free, self-hosted MCP server that tells your agent what LLMs cite - across Perplexity, Google AI Overviews, ChatGPT, Claude, Gemini, and Bing.

What this is

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.

Who this is for

Install this if you're:

• Building an agent that does research and want it to cite sources LLMs already trust
• A solo dev or indie hacker checking whether your SaaS is showing up in AI search
• A content creator confirming your articles are being cited by ChatGPT, Claude, or Perplexity
• An SEO or GEO practitioner who wants programmatic citation data without a $295-$499/mo dashboard
• Running an editorial pipeline and want citation-deficit-driven topic selection
• Comparing competitor visibility across AI engines for any niche

Do NOT install this if you want:

• A polished marketing dashboard with charts and team seats - try Profound, AthenaHQ, or Otterly.AI
• A hosted service with SLAs - this is self-hosted by design
• Citation tracking for academic papers - try citecheck
• 350M+ pre-modeled prompts - that's Ahrefs Brand Radar

Why this exists

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.

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Tools

Tools are grouped into six namespaces: citations.*, domain.*, signals.*, panel.*, competitors.*, audit.*. Names form a navigable tree — pick a namespace by the question you're asking.

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

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

Prompts

Server-side prompt templates the client can offer end users (call via the MCP prompt list):

• audit.citation_readiness(url) - chains citations.predict + audit.schema
• audit.competitor_snapshot(query, your_url?) - chains competitors.canonical_set + competitors.compete
• audit.crawler_checkup(url) - runs audit.crawler_access and writes a remediation list
• audit.gap_analysis(domain, days?) - drives signals.gsc_gap and suggests next moves
• audit.sitemap_coverage(sitemap_url) - runs audit.sitemap_map and recommends priorities

Resources

Cache views the client can read or subscribe to (no tool call required):

• citation://cache/summary - entry counts by type/engine, unique queries/URLs, oldest/newest
• citation://panels - saved panels + per-panel snapshot counts
• citation://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}

What this actually measures

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.

Per-engine notes

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-search-preview via the OpenAI Responses API. This is the model OpenAI ships to mirror SearchGPT behavior — closer to consumer than gpt-4o-mini, but still API-tier.

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.

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Quick start

npx -y @automatelab/citation-intelligence

Requires Node 20 or later.

Claude Desktop

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.

How it stays free

• No central backend. The server runs on your machine. Nothing is uploaded.
• Free tier first. SerpAPI gives 100 free Google AI Overview lookups/month. Bing Web Search has a free tier. Perplexity offers free Sonar access on signup.
• Bring your own paid keys if you want the premium engines (Claude, ChatGPT, Gemini). Keys pass through to the vendor and never touch any third party.
• Local cache at ~/.config/citation-intelligence/cache.json. Repeated queries hit cache, not API. Default TTL: 7 days.
• citations.predict runs with zero keys - it scores citation likelihood from public signals (Wikipedia, schema.org, llms.txt, GitHub) without firing any LLM.

Privacy

• All API calls go from your machine directly to the vendor (Anthropic, OpenAI, Google, Perplexity, Bing, SerpAPI).
• No proxy. No analytics. No telemetry by default.
• API keys are read from environment variables on the MCP process - never logged, never persisted.
• Cache file lives at ~/.config/citation-intelligence/cache.json. Delete it any time.

Environment variables

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)	100/month free
ANTHROPIC_API_KEY	citations.check (claude — api_proxy)	Paid only
OPENAI_API_KEY	citations.check (openai — api_proxy)	Paid only
GEMINI_API_KEY	citations.check (gemini — api_proxy)	Yes
BING_API_KEY	citations.check (bing_serp — web_rank)	Yes
BRAVE_API_KEY	citations.check (brave_serp — web_rank)	Yes (2000/month)
CITATION_CACHE_TTL_DAYS	Cache TTL for citations.check entries (default 7)	n/a
CITATION_AI_OVERVIEW_TTL_DAYS	Cache TTL for signals.ai_overview entries (default 1)	n/a
CITATION_CONFIG_DIR	Override config dir (default ~/.config/citation-intelligence)	n/a

Example: am I cited?

You: For the queries "best AI citation tracker", "MCP for AI search", "self-hosted GEO tool",
     is automatelab.tech cited?

(agent invokes `domain.am_i_cited`)

Result:
{
  "domain": "automatelab.tech",
  "engine": "perplexity",
  "results": [
    { "query": "best AI citation tracker",   "cited": true,  "rank": 4 },
    { "query": "MCP for AI search",          "cited": true,  "rank": 1 },
    { "query": "self-hosted GEO tool",       "cited": false, "matching_urls": [] }
  ],
  "summary": {
    "queries_total": 3,
    "queries_cited": 2,
    "citation_rate": 0.67,
    "average_rank": 2.5
  }
}

Example: predict citation likelihood (no key required)

You: How likely is https://example.com/blog/post to be cited by AI?

(agent invokes `citations.predict`)

Result:
{
  "url": "https://example.com/blog/post",
  "score": 62,
  "grade": "C",
  "signals": {
    "wikipedia_linked": false,
    "github_referenced": false,
    "reddit_referenced": true,
    "llms_txt_present": true,
    "https": true,
    "has_article_schema": true,
    "has_faq_schema": false,
    "has_breadcrumb_schema": true,
    "canonical_clean": true,
    "word_count": 1850,
    "reading_time_minutes": 8,
    "h2_count": 7,
    "h2_question_count": 1,
    "authority_link_count": 2,
    "external_link_count": 6,
    "internal_link_count": 11,
    "last_modified_days_ago": 42,
    "has_open_graph": true
  },
  "fixes": [
    { "signal": "has_faq_schema", "suggestion": "Page already has question-style H2s. Wrap them in FAQPage JSON-LD - high-leverage win.", "estimated_lift": "high" },
    { "signal": "h2_question_count", "suggestion": "Reframe at least 2 H2s as questions users actually ask...", "estimated_lift": "medium" }
  ]
}

The Wikipedia signal is measured (it correlates with citation) but no "go get a Wikipedia article" suggestion is emitted - the advice would be non-actionable. Scoring is split across six buckets - domain authority, structured data, content depth, link graph, freshness, metadata - so a thin page and a deep page on the same domain get meaningfully different scores.

---

Workflow recipes

Concrete patterns that compose the 24 tools into something useful. Costs assume ChatGPT or Perplexity at ~$0.01-0.03/query.

1. Weekly citation tracker

The single highest-ROI pattern. Pick 20-30 queries from your editorial backlog, snapshot weekly, watch the rate trend.

# One-time setup
panel.track name="editorial-watchlist" domain="example.com" action="save"
            queries=["best widget tutorial", "how to set up X", ...]

# Weekly cron (5 min, ~$0.20-0.60 per run)
panel.run name="editorial-watchlist"

# Anytime
citations.trend panel="editorial-watchlist"

citations.trend returns per-query deltas: which queries flipped from cited: false to cited: true since the first snapshot. That's your real editorial-impact metric.

2. Pre-publish gate

Before publishing a post, find out who owns the citation slot and whether the slot is worth competing for.

# 1. Is there an AI Overview to compete for?
signals.ai_overview query="<target query>"

# 2. Who is cited today?
citations.check query="<target query>"

# 3. After publish + 14 days: did the post break in?
domain.am_i_cited domain="example.com" queries=["<target query>"]

If citations.check returns 5+ strong incumbents on a low-volume query, pick a different angle. If ai_overview_present: false, the query has no AI surface - reconsider.

3. Bulk site audit

Catch site-wide structural issues across every page in one pass. Zero API spend.

audit.sitemap sitemap_url="https://example.com/sitemap.xml" limit=200

Returns worst_first sorted by citation-likelihood score. Surfaces missing schema, conflicting canonicals, missing /llms.txt, broken HTTPS.

4. Competitor signal gap

You're not cited; they are. Why?

# 1. Find the top-cited URLs for your target query
citations.check query="<query>"

# 2. Compare your URL to theirs signal-by-signal
competitors.compare urls=[
  "https://example.com/your-post",
  "https://competitor-1.com/their-post",
  "https://competitor-2.com/their-post"
]

diverging_signals is the list of where you're losing. Usually obvious once you see it - they have FAQ schema, GitHub references, Wikipedia links - you don't.

5. Google-rank vs AI-citation gap

The closest editorial wins are queries where you already rank in Google's top 10 but are invisible to AI. Requires a GCP service account with webmasters.readonly scope.

signals.gsc_gap
  domain="example.com"
  queries=["...editorial watchlist..."]
  start_date="2026-04-01"
  end_date="2026-05-01"

closest_wins returns queries with position <= 10 and ai_cited: false, sorted by impressions desc. Push citation signals on those specific URLs first.

6. Wikipedia mention monitor

Wikipedia is the top-correlation signal but the advice "get on Wikipedia" is useless. So instead: watch when it happens organically.

signals.wikipedia domain="example.com" limit=50

Returns Wikipedia article URLs that already link to the domain. Re-run quarterly; the diff is your "we got a Wikipedia citation" alert.

Schema.org

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Citation Intelligence MCP",
  "applicationCategory": "DeveloperApplication",
  "operatingSystem": "Cross-platform",
  "description": "Self-hosted MCP server for querying AI citation data from Perplexity, Claude, ChatGPT, Gemini, Bing, and Google AI Overviews.",
  "offers": { "@type": "Offer", "price": "0" },
  "url": "https://github.com/AutomateLab-tech/citation-intelligence"
}

Contributing

Bug reports, feature ideas, and PRs welcome. See CONTRIBUTING.md.

Security

Report a vulnerability via SECURITY.md.

License

MIT - see LICENSE.

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