中文 AI 可引用性（GEO/AEO）体检 MCP Server

by savantcat Your server? Claim it

not responding

中文 AI 可引用性（GEO/AEO）体检 is listed as active in the registry but did not answer our last check. It exposes 7 tools. Last commit 21 Oct 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、观测时序追踪

Uptime history 52 days of history · worst day 78%

52 days agonow

80.2%

Uptime 24h

73 of 91 checks

Tools

read from the server

1621 ms

Response time

average over 24h

Stars

last commit 21 Oct 2026

What changed 3

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 4 October 2026. No other catalogue keeps this.

4 Oct 3 tools appeared diff_observations, list_signals, query_history

What the code does

We read the source, 1 d ago · tools taken from the live server · rules 995398eba69f

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:95

    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.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 12 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add geo-cn --transport http https://savantcat.cn/mcp-geo

Settings → Connectors → Add custom connector, then paste this address

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

{
  "servers": {
    "geo-cn": {
      "type": "http",
      "url": "https://savantcat.cn/mcp-geo"
    }
  }
}

Available tools 7

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。

diff

diff_observations

对比某 identity 的**最近两次观测**，判定提升/退化/中性（带阈值与样本门槛说明）。判定门槛：相对变化 >= 20% **且** 绝对变化 >= 0.05 才算"显著"（severity: rise / sharp_drop）。样本 n < 20 一律返回 severity=low_n，**不给出升降结论**——这是防误报的硬门。 Args: identity: 品牌名或域名 kind: 观测类型，留空取全部 series: 题集/口径标识，留空取全部

history

query_history

查某个品牌/域名在**历史观测**中的时序（多次采样的轨迹）。数据来自本工作室按周期采样的记录（写入走 CLI，服务本身只读）。 **只在同一 series（同一题集/同一口径）内纵向比较**，不同 series 的值不可直接比大小。 Args: identity: 品牌名或域名，如 "合尘猫" / "savantcat.cn" kind: 观测类型，留空取全部。常用：mention_rate(提及率) / citation_rate(引用率) / visibility_score(可见度分) / rank(排名) / robots_policy / llms_txt series: 题集/口径标识，留空取全部 limit: 每条 series 最多返回的最新点数

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。

signals

list_signals

列出**已检测到的变化信号**（visibility 提升/退化等），带生命周期（first_seen / resolved_at）。 Args: status: open（未解决，默认）/ resolved（已解决）/ all limit: 最多返回条数

Endpoints

URL	Transport	State	Latency	Checked
https://savantcat.cn/mcp-geo	streamable-http	answering	8456 ms	12 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 7 tools, read directly from the server on our last check. Among them: audit_cn_citability, diff_observations, list_signals, plan_fixes, probe_source_pool, query_history and 1 more. 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 73 of 91 checks got a reply (80.2%), average response time 1621 ms. The bar chart above shows every period we have measured.

The registry lists 中文 AI 可引用性（GEO/AEO）体检 as active — why does it not respond?

The official MCP registry stores what the author submitted; it does not verify that the server still runs. We check the endpoint ourselves, and this one does not answer. Catalogues that copy the registry without checking will show it as working.

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 in the form 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 7 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 1621 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?

The code is open source: it is published under the Apache-2.0 licence, written in Python and 1 stars on GitHub. The licence covers the code in the repository, not the hosted service at savantcat.cn, which runs on its operator's own terms.