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Citation Intelligence

作者:AutomateLab-tech版本 0.12.1i★ –i在你的电脑上运行
注册名称io.github.AutomateLab-tech/citation-intelligence

安装

概览

作者提供的说明(英文原文)

Check what Perplexity, Claude, ChatGPT, Gemini, and Google AI Overviews cite for any query.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "citation-intelligence": {
      "command": "npx",
      "args": [
        "-y",
        "@automatelab/citation-intelligence"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add citation-intelligence -- npx -y @automatelab/citation-intelligence

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "citation-intelligence": {
      "command": "npx",
      "args": [
        "-y",
        "@automatelab/citation-intelligence"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "citation-intelligence": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@automatelab/citation-intelligence"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 AutomateLab-tech 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 7249 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7249 个服务器的 60% 更新
  • 基础检查结果等于或优于此分类中其他 7249 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Citation Intelligence 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.12.1)于24天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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.


Tools

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
ToolPurpose
citations_provenanceRecommended first tool. Fan a query across engines; per-URL cross-engine consensus matrix. Returns interpretation_note per engine.
citations_checkURLs cited by Perplexity / Claude / ChatGPT / Gemini / Google AI Mode for a query; or web rank via bing_serp / brave_serp
citations_evidenceExtract the cited snippet from raw_answer for each citation (why, not just that)
citations_predictCitation likelihood from public signals - no LLM fired
citations_trendTime-series report of citation rate + per-query gained/lost deltas
citations_freshnessRecency score (halflife=365d) for the pages an engine cites
domain_* — domain-level: am I cited, what for
ToolPurpose
domain_am_i_citedDomain 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_forQueries the domain has been cited for, from local cache
domain_cited_for_diffDiff of domain_cited_for between two time windows for a domain
signals_* — external signals: AI Overview, Wikipedia, GSC, answer-box position
ToolPurpose
signals_ai_overviewGoogle AI Overview presence + cited sources
signals_wikipediaList Wikipedia articles referencing a domain (zero keys)
signals_gsc_gapJoin Google Search Console performance with AI citation status
signals_answer_boxBin each citation's first mention in raw_answer into early/middle/late thirds
panel_* — saved query panels (editorial watchlists)
ToolPurpose
panel_trackSave / load / list named query panels (editorial watchlists)
panel_runRun a panel through domain_am_i_cited and snapshot to disk
report_* — turnkey reporting artifacts
ToolPurpose
report_visibilityOne-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
ToolPurpose
competitors_canonical_setTop cited domains per query, aggregated across engines
competitors_competeEnd-to-end competitive snapshot: your URL vs top cited competitors
competitors_compareSide-by-side citations_predict across 2-10 URLs
audit_* — fixable on-page / on-site checks
ToolPurpose
audit_schemaDeep schema.org validation - required fields per @type, malformed JSON-LD
audit_structured_dataRepair-oriented schema.org diagnostics + suggested patches
audit_crawler_accessVerify GPTBot / ClaudeBot / PerplexityBot / CCBot / Google-Extended etc. can fetch a URL
audit_sitemapBulk citations_predict across every URL in a sitemap, worst-first
audit_sitemap_mapCross-reference sitemap URLs with cached citations (inverse of audit_sitemap)
audit_llms_txtGenerate 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.

SurfaceEnginesWhat it means
consumer_scrapeperplexity, google_ai_modeProxied through a real consumer-facing AI search product. Closest to what your users see.
api_proxyclaude, openai, geminiAPI 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_rankbing_serp, brave_serpTraditional web search rank (not LLM citation). Measures whether a URL appears in SERP results, not whether an LLM cites it.
static_signalcitations_predict, signals_wikipediaOffline 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 + 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.


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

VarPurposeFree tier?
PERPLEXITY_API_KEYcitations_check (perplexity — consumer_scrape)Yes

| SERPAPI_KEY | signals_ai_overview + citations_check (google_ai_mode — consumer_scrape)

只显示了开头部分;其余内容请在仓库中阅读

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