found-by-ai-monitor

Live AI visibility measurements from Are you found by AI?, queryable by your own AI.

我該用這個嗎

品質與安全性

B
說明品質
93%
結構描述完整度
45%
命名品質
99%
汙染風險
100%
權限相符程度
100%
協定合規性
100%

根據工具定義與協定合規性的自動化分析。

上下文成本

~2,396Token(工具定義)
~372 B典型回應大小
中等的注意力影響(128k 上下文的 1.87%)

這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。

安裝

一鍵安裝

將以下內容加入你的 `claude_desktop_config.json` 檔案:

{
  "mcpServers": {
    "found-by-ai-monitor": {
      "url": "https://areyoufoundbyai.com/mcp/demo"
    }
  }
}

遠端端點

https://areyoufoundbyai.com/mcp/demostreamable-http

它能做什麼

工具清單

工具(20)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢get_visibility

The latest AI Visibility and AI Readiness scores (each /100) for areyoufoundbyai.com, with the previous week's scores, the separate off-site Footprint score, and the subscores (citability, E-E-A-T, technical, schema, platform compose Readiness; Footprint sits beside it). Being named in the answer prose counts in full toward your visibility score. A positive result recorded only as a list entry, citation or source title counts half. Older results without this distinction retain their recorded scoring basis. Named: your business is named in the answer. Cited: your website is linked in the answer’s sources. Listed: your name appears in a list, heading or source title rather than in the prose. Found via search: a page appears in recorded search results; that alone is not a citation or recommendation. These can overlap. A web-search flag only records that search was used. Older positive flags may not distinguish these types; read the retained answer. Unavailable is not a negative result.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_question_trajectories

Every tracked buyer question with its measured history: how many of the 7 engines named the business on each measured day. This is where visibility is actually won or lost, question by question.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_personas

The 2-4 buyer personas inferred from this business's measured questions, the way AI models would describe each buyer type, with the exact tracked questions each persona asks. Read-only.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_mentions(days)

New pages on the web that mention the business, from the daily sweep. Independent mentions are the strongest signal that moves AI answers.

輸入結構描述

{
  "type": "object",
  "properties": {
    "days": {
      "type": "number",
      "description": "Look-back window in days (1-90, default 30)"
    }
  },
  "additionalProperties": false
}
🟢get_rivals

The competitor names the answer engines actually gave in the latest scan when they did not name this business.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_cited_queries(competitor, engine, since, limit)

Reverse lookup over every measurement we have already run: give a competitor's brand name or domain and get the buyer questions where the answer engines named them, which engines, how often, when they were last seen, and who else was named on those questions. Also reports where that domain was cited as a source. Coverage is the questions we have measured, so a thin result means we have not asked those questions yet, never that the competitor is absent from AI.

輸入結構描述

{
  "type": "object",
  "properties": {
    "competitor": {
      "type": "string",
      "description": "A brand name or domain, e.g. Rocketlane or rocketlane.com"
    },
    "engine": {
      "type": "string",
      "description": "Optional: only questions where this engine named them"
    },
    "since": {
      "type": "string",
      "description": "Optional ISO date, only measurements on or after it"
    },
    "limit": {
      "type": "integer",
      "description": "Maximum questions, default 25, max 100"
    }
  },
  "required": [
    "competitor"
  ],
  "additionalProperties": false
}
🟢get_schema_evidence(offset, limit, chars)

Initial HTML versus rendered JSON-LD evidence, with read dates, incomplete states, excerpts and limitations. Reads stored evidence only; no provider request. Page findings with offset and limit.

輸入結構描述

{
  "type": "object",
  "properties": {
    "offset": {
      "type": "integer",
      "minimum": 0
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 40
    },
    "chars": {
      "type": "integer",
      "minimum": 120,
      "maximum": 12000
    }
  },
  "additionalProperties": false
}
🟢get_regional_visibility

Saved areas, buyer questions and engine naming observations, retained samples and read coverage. Unknown and failed reads are distinct from not named. No fresh provider requests.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_answers(offset, question, engine, limit, chars)

The exact answer each engine gave to each tracked buyer question in the latest deep measurement for areyoufoundbyai.com: engine, model, date, whether this business was named and on how many samples, live web search or model memory, the competitors named in that answer, the web searches the engine ran before answering (fan-out), and the question's Google demand. This is the receipt behind every score. Filter by a question substring or an engine; answers are trimmed to `chars` characters. Named: your business is named in the answer. Cited: your website is linked in the answer’s sources. Listed: your name appears in a list, heading or source title rather than in the prose. Found via search: a page appears in recorded search results; that alone is not a citation or recommendation. These can overlap. A web-search flag only records that search was used. Older positive flags may not distinguish these types; read the retained answer. Unavailable is not a negative result.

輸入結構描述

{
  "type": "object",
  "properties": {
    "offset": {
      "type": "integer",
      "description": "Question offset from nextOffset; default 0"
    },
    "question": {
      "type": "string",
      "description": "Substring of a tracked question, case-insensitive"
    },
    "engine": {
      "type": "string",
      "description": "One of ChatGPT, Perplexity, Gemini, Claude, Grok, DeepSeek, Google AI Overviews"
    },
    "limit": {
      "type": "integer",
      "description": "Maximum question rows, default 12, max 25"
    },
    "chars": {
      "type": "integer",
      "description": "Maximum characters per answer, default 4000, max 12000"
    }
  },
  "additionalProperties": false
}
🟢get_share_of_voice

Two measurements, kept apart. First, your share of mentions in your own tracked answers against the rivals the engines named there. Second, web mention share: how often a web-scale index of ChatGPT answers mentions you against the brands we compare you with, which is not a rival list. Use get_rivals for who the engines name instead of you.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_context

The complete weekly context document: scores, every tracked question with its verbatim answer status, mentions, and what to do next. Same content as the downloadable context.md.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_fix_plan

The prioritised fix list from the latest deep scan: the specific changes that move this business up in AI answers, highest impact first.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_citation_sources

The domains AI engines actually cite in answers matching this category, most-cited first, with whether this business appears on each. Getting mentioned there moves visibility more than on-site changes.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_crawler_access

Whether this business's robots.txt allows or blocks each documented AI crawler: OpenAI, Anthropic, Google, Perplexity, Apple, Microsoft, Meta and Common Crawl, split by what each one is for (search, model training, or fetching a page because a user asked). Read from the stored evidence of the latest deep scan, with the matching robots.txt rule quoted for each. Also names vendors this checker cannot assess, so an agent does not read silence as permission.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_source_profile(source)

A profile of any domain the answer engines cite: how often our measurements saw engines read it and across how many businesses and categories, what the site says it is (title and description from our crawl of its homepage), the co-read pack it travels in (sources the engines read together), and, where our crawler found one, the page where a business gets listed on it. Aggregate market data from our measurement corpus; a thin result means the engines rarely cite it in what we have measured so far.

輸入結構描述

{
  "type": "object",
  "properties": {
    "source": {
      "type": "string",
      "description": "The source domain, e.g. canstar.com.au or yelp.com"
    }
  },
  "required": [
    "source"
  ],
  "additionalProperties": false
}
🟢get_post_brief

Everything an AI needs to draft social posts that move AI visibility, assembled from this week's measured data: fresh third-party mentions to anchor on, the exact buyer questions the engines answer without naming the business (and who they name instead), the domains the engines actually read, and the entity rules that make a post retrievable. Returns a drafting brief, never generated copy: the drafting happens in your AI, in the business's own voice.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_ai_traffic

Visitors the AI engines actually sent to the business's site in the last 30 days, recorded by the site's own beacon: totals by engine and by week, with the tracking wiring status. This closes the loop from being named in answers to humans arriving.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_agent_view

A headless agent-browser read of the site (refreshed daily): full, partial or blank, with how many characters of real content an agent can extract. A site that renders blank to agents is invisible to agentic AI regardless of content quality. No other tool in this lane measures it.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢get_benchmark

Where this business sits against every other measured business in its category: rank, percentile, and the distribution above and below. Real corpus, not an estimate.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
🟢request_rescan

DEMO: returns a worked example of the response and queues nothing. Queue a fresh deep measurement right now instead of waiting for the weekly scan. Uses one of the plan's capped on-demand re-measures; runs the measurement and changes nothing else. Results land in a few minutes, then read get_visibility again.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

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