Crawl Census

Ask before you fetch: will this domain serve your crawler, refuse it, or charge it?

我該用這個嗎

品質與安全性

B
說明品質
97%
結構描述完整度
80%
命名品質
83%
汙染風險
80%
權限相符程度
100%
協定合規性
100%

發現項目(2)

  • HIGHTool poisoning patterns detected
  • MEDIUMTool description contains URL to non-standard domain在 crawl_preflight 中

根據工具定義與協定合規性的自動化分析。

上下文成本

~963Token(工具定義)
~482 B典型回應大小
中等的注意力影響(128k 上下文的 0.75%)

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": {
    "crawl-census": {
      "url": "https://crawlcensus.com/mcp"
    }
  }
}

遠端端點

https://crawlcensus.com/mcpstreamable-http

它能做什麼

工具清單

工具(7)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢scan_site(domain)

Run a live AI-accessibility audit of a domain: robots.txt policy for every tracked AI crawler, live user-agent probes, JavaScript-free readability, structured data and llms.txt. Returns a score out of 100 with per-check detail.

輸入結構描述

{
  "type": "object",
  "properties": {
    "domain": {
      "type": "string",
      "description": "Bare hostname, for example example.com"
    }
  },
  "required": [
    "domain"
  ]
}
⚪site_report(domain)

Return the most recent stored audit for a domain without triggering a new scan. Faster and free of load on the target site.

輸入結構描述

{
  "type": "object",
  "properties": {
    "domain": {
      "type": "string",
      "description": "Bare hostname"
    }
  },
  "required": [
    "domain"
  ]
}
⚪census_stats

Corpus-level statistics: how many measured domains block each AI crawler, mean access score, llms.txt adoption.

輸入結構描述

{
  "type": "object",
  "properties": {}
}
🟢crawl_preflight(agent, domains)

Decide whether a crawler may fetch a list of domains before spending requests on them. Works for any crawler token, not only the ones this census tracks: an unrecognised agent is resolved from each domain's stored robots.txt rather than refused. For each domain returns one of: allow (robots permits it and a live request carrying that agent's user agent was served), disallow (robots.txt forbids it), refuse (robots permits it but the edge refused the agent anyway, so the allowance is not real), pay (the origin answered HTTP 402 Payment Required, meaning it will serve this agent on commercial terms), or unknown. The full definition of each, including what it obliges a crawler to do, is published at https://crawlcensus.com/api/v1/verdicts. Built for crawler operators rather than site owners: it prevents wasted fetches against doors that are shut, and flags content an operator is trying to sell rather than withhold.

輸入結構描述

{
  "type": "object",
  "properties": {
    "agent": {
      "type": "string",
      "description": "Crawler token, e.g. gptbot, claudebot, perplexitybot, oai-searchbot, ccbot."
    },
    "domains": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Domains to check. Up to 25 per call anonymously; send an Authorization: Bearer key for more. An over-large batch is refused outright rather than partly answered."
    }
  },
  "required": [
    "agent",
    "domains"
  ]
}
⚪agent_profile(agent)

What this census measures and publishes about one AI crawler: how often it is disallowed in robots.txt, how often live requests carrying its user agent are refused at the network edge whatever robots.txt says, whether its operator documents it as honouring robots.txt, and where to correct any of that. Intended for the operator of the agent as much as for anyone studying it, so it includes the correction channel and the public page a claim can be disputed against.

輸入結構描述

{
  "type": "object",
  "properties": {
    "agent": {
      "type": "string",
      "description": "Crawler token, e.g. gptbot, claudebot, ccbot, google-extended."
    }
  },
  "required": [
    "agent"
  ]
}
⚪census_facts

Every headline finding from the census as discrete, dated records rather than prose. Each carries its value, unit, denominator, measurement date, the page it comes from and a ready-made citation line, plus the caveats that apply to all of them. Use this when answering a question about how open the web is to AI crawlers: lifting a percentage out of a rendered page loses the denominator and the date, which is what makes the number wrong when it is repeated.

輸入結構描述

{
  "type": "object",
  "properties": {}
}
🟡submit_domains(domains)

Queue domains the census has not measured yet so a later crawl_preflight can answer them. This closes the loop crawl_preflight starts: anything it returns as unknown with measurable true is worth submitting, and the reply names any that were already fresh or that this census will never measure, so a caller looping over its own unknowns converges instead of resubmitting the same set. Queueing is a database write rather than a fetch, so the allowance is far higher than scan_site and submitted domains are measured ahead of the ranked backlog.

輸入結構描述

{
  "type": "object",
  "properties": {
    "domains": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Hostnames to queue. Up to 50 per call anonymously; an over-large batch is refused outright rather than partly queued."
    }
  },
  "required": [
    "domains"
  ]
}

社群

為此伺服器評分

證據

近期觀測

已驗證未記錄版本7 個工具
已驗證未記錄版本7 個工具
已驗證未記錄版本7 個工具