Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Check what Perplexity, Claude, ChatGPT, Gemini, and Google AI Overviews cite for any query.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"citation-intelligence": {
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
]
}
}
}ターミナルでこれを実行します
claude mcp add citation-intelligence -- npx -y @automatelab/citation-intelligenceプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"citation-intelligence": {
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"citation-intelligence": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@automatelab/citation-intelligence"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント AutomateLab-tech で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.12.1)がレジストリに届いたのは24 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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)
冒頭だけを表示しています。続きはリポジトリで読めます