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中文 AI 可引用性(GEO/AEO)体检 MCP Server

answering

中文 AI 可引用性(GEO/AEO)体检 is answering right now. Last checked 10 min ago. It exposes 4 tools. Last commit 19 Sep 2026.

中文站能否被豆包/DeepSeek/文心/Kimi 抓取、读懂、引用:可引用性审计、信源池占位探测、五大指标测量、修复计划

Uptime history 71 hours of history · worst hour 25%
71 hours agonow
87.9%
Uptime 24h
80 of 91 checks
4
Tools
read from the server
1735 ms
Response time
average over 24h
0
Stars
last commit 19 Sep 2026

What the code does

We read the source, 23 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

Builds a file path from a variable server.py:67
    p = os.path.join(DATA, name)

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 10 min ago.

run in your terminal
claude mcp add geo-cn --transport http https://savantcat.cn/mcp-geo
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "geo-cn": {
      "url": "https://savantcat.cn/mcp-geo"
    }
  }
}
~/.codex/config.toml
[mcp_servers.geo-cn]
url = "https://savantcat.cn/mcp-geo"
.cursor/mcp.json
{
  "mcpServers": {
    "geo-cn": {
      "url": "https://savantcat.cn/mcp-geo"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "geo-cn": {
      "url": "https://savantcat.cn/mcp-geo"
    }
  }
}

Available tools 4

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

audit
audit_cn_citability
审计一个网站/页面能否被中文 AI 搜索(豆包、DeepSeek、文心、Kimi 等)抓取、解析与引用。 一次抓取完成六层检查:可抓取(含中国爬虫矩阵与边缘层拦截实测)、可解析、可引用、 实体一致、分发、可信可自验;返回 0-100 分与 A-F 等级、逐项证据、优先修复清单。 用途场景:客户站点体检、上线前自检;传 compare_with 可做竞品对比(逐层与逐项差异)。 注意:只做确定性检查,不调用大模型;评分口径见 scoring_version。
plan
plan_fixes
按缺口生成优先修复计划(可直接交给客户或工程执行)。 两种用法:① 传 fail_ids(逗号分隔的自查项编号,如 "L1-3,L2-4");② 传 url,工具先审计再出计划。 返回:按权重排序的修复项、每项「为什么」「怎么补」「验收方式」。
probe
probe_source_pool
探测一个中文问题在信源池里的实际占位分布,并判断你的品牌/域名是否在池子里。 原理:AI 答案只能引用「已进入检索池」的来源。若目标问题下主流信源被平台型站点 (知乎/公众号转载站/百家号/CSDN 等)占满而你不在其中,再好的站内优化也不会被引用。 返回:结果域名分布、平台归类、是否命中你的品牌/域名、同题竞品域名清单、建议动作。 参数:question 用户真实会问的问题原话;brand 品牌词(如「合尘猫」);domain 你的域名(如 savantcat.cn)。
score
score_visibility
按五大核心指标 + 语义角色分权,计算 AI 搜索可见度并输出可自验的测量报告。 输入 samples 为 JSON 字符串: {"brand":"合尘猫","domain":"savantcat.cn","baseline_negative":3, "fact_points":["服务范围","交付周期","定价方式"], "samples":[{"question":"Q1","platform":"DeepSeek","run":1,"answer":"原文回答……", "role":"independent|joint|citation_only|none", "sentiment":"positive|neutral|negative", "facts":"accurate|partial|wrong|unverifiable", "facts_found":["服务范围"],"as_of":"2026-09-19"}]} 口径:每题建议多轮(同一问题重复 7-8 次);role 分级对应语义角色权重; 返回五大指标、语义角色加权分、逐题明细与「原文摘录」(便于客户自验)。 verify_citations=True 时会实测 AI 回答里引用的 URL 是否真的可访问(幻觉守卫); extract_claims=True 时会把回答中的数字声明单独列出并标记为未核验。

Endpoints

URLTransportStateLatencyChecked
https://savantcat.cn/mcp-geo streamable-http answering 1826 ms 10 min ago

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中文 AI 可引用性(GEO/AEO)体检 — questions

Answers built from our own checks of this server.

What can 中文 AI 可引用性(GEO/AEO)体检 do?
It exposes 4 tools, read directly from the server on our last check. Among them: audit_cn_citability, plan_fixes, probe_source_pool, score_visibility. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is 中文 AI 可引用性(GEO/AEO)体检 working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 80 of 91 checks got a reply (87.9%), average response time 1735 ms. The bar chart above shows every period we have measured.
How do I connect 中文 AI 可引用性(GEO/AEO)体检?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does 中文 AI 可引用性(GEO/AEO)体检 need an API key?
No. 中文 AI 可引用性(GEO/AEO)体检 completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 4 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is 中文 AI 可引用性(GEO/AEO)体检?
It answers our handshake in 1735 ms on average, which is faster than 1% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is 中文 AI 可引用性(GEO/AEO)体检 open source?
We cannot say either way: written in Python and 0 stars on GitHub, but we could not determine the licence, and without one the code is not open source by default.