citehound

endpoint

https://getcitehound.com/api/mcp

protocol

streamable-http ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is citehound live?

Yes — it answered the hub's last check (checked 38m ago). It answered 100% of checks over the last 30 days.

Is citehound free to use?

Yes — the hub reached it with no key and no payment.

What tools does citehound have?

13 tools: compare_sites, generate_schema, get_research, scan_site, generate_llms_txt, get_playbook, list_ai_crawlers, get_citation_prompts, ….

Is citehound safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• get_benchmark open 38m ago

  Returns Citehound’s own archived scan data for one category — average, median and range of scores, pillar breakdowns, per-crawler blocked, limited and open counts, and the most common failed checks, with sample size and scan date — or a ranked summary across every category when called with no argument. This is archived data, not a live scan, and fetches nothing external.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "category": {
        "type": "string",
        "description": "Optional. One of: crm, cybersecurity, devtools, dtc-brands, consumer-apps, hospitality. Omit to get every category."
      }
    }
  }

  arguments 9 lines copy
• get_citation_sample open 38m ago

  Returns Citehound’s anonymised sample citation run as readable text: the model, the date, how many times each question was asked, the questions in three groups (named in every try, unstable, never named), the overall rate of being named with its range across sweeps, and a note on the limits. It is one model on one date, so it is not a ranking, says nothing about other assistants and is not a forecast of traffic. It names no brand in the category being measured, no domain and no competitor. Read-only: it reads a content file and queries no model. Use when the user wants to see what a citation result looks like, or asks how much the same question varies between tries.

  mcp-tool

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

  arguments 4 lines copy
• compare_sites unknown never probed

  Scans two domains — one after the other, not in parallel — and reports both scores plus every check where they differ. Useful for comparing your site against a named competitor.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "domain_a",
      "domain_b"
    ],
    "properties": {
      "domain_a": {
        "type": "string",
        "description": "The first bare domain, e.g. \"example.com\"."
      },
      "domain_b": {
        "type": "string",
        "description": "The second bare domain, e.g. \"competitor.com\". Must differ from domain_a."
      }
    }
  }

  arguments 17 lines copy
• generate_schema unknown never probed

  Fetches a domain’s homepage for its real title, meta description and declared language, then returns ready-to-paste JSON-LD structured data of the requested type. Fields the scan cannot know — a FAQ’s actual questions, a product’s real price — come back as clearly bracketed placeholders, never invented values.

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  {
    "type": "object",
    "required": [
      "domain",
      "type"
    ],
    "properties": {
      "type": {
        "enum": [
          "organization",
          "faqpage",
          "article",
          "product",
          "localbusiness"
        ],
        "type": "string",
        "description": "\"organization\" returns Organization + WebSite schema as an @graph; the others (faqpage, article, product, localbusiness) each return one schema of that @type."
      },
      "domain": {
        "type": "string",
        "description": "A bare domain, e.g. \"example.com\"."
      }
    }
  }

  arguments 24 lines copy
• get_research unknown never probed

  Returns Citehound’s published research: with no argument, the list of reports (the llms.txt and crawler study, the AgaOne case study and the benchmark data) with title, date, one-line finding and URL; with a slug, the key findings, the method limits, the URL and a ready line for citing it. Findings are checked against the published pages and data files. Every study measures readiness inputs, not whether any assistant names a brand. Read-only, fetches nothing external. Use when the user wants evidence for a claim about AI crawler access or llms.txt, a statistic to cite, or the reports behind the benchmarks.

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  {
    "type": "object",
    "properties": {
      "slug": {
        "type": "string",
        "description": "Optional. A report slug from the list this tool returns with no argument. Omit to list the reports."
      }
    }
  }

  arguments 9 lines copy
• scan_site unknown never probed

  Scans a domain’s public robots.txt, llms.txt, sitemap declaration and homepage, and scores it out of 100 across three pillars: discoverability (can AI crawlers reach it), technical foundation (can machines parse it), and content & trust (does it look like a credible source). Returns the score, the three pillar scores, access state for the 10 tracked AI crawlers, every failed check with its fix, and how the score compares to Citehound’s own benchmark data. If the site looks like a store (two or more of five signals), it also reports a separate agentic-commerce sub-score: whether a UCP merchant profile exists at /.well-known/ucp, whether one product page has Product schema with name, price, availability and image, and whether the llms.txt looks like a platform default. That sub-score is never part of the 100. Call this first for any domain. It fetches the live site (a store scan reads up to one extra product page), so avoid calling it in a tight loop for the same domain.

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  {
    "type": "object",
    "required": [
      "domain"
    ],
    "properties": {
      "domain": {
        "type": "string",
        "description": "A bare domain, e.g. \"example.com\". No scheme (https://) and no path — the scan always targets the domain root."
      }
    }
  }

  arguments 12 lines copy
• generate_llms_txt unknown never probed

  Builds an llms.txt file — an optional convention for describing a site and its key pages, with no demonstrated effect on Google — from a name, a one-sentence description and a list of key pages. Pure text generation — fetches nothing.

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  {
    "type": "object",
    "required": [
      "name",
      "description"
    ],
    "properties": {
      "name": {
        "type": "string",
        "description": "The site or product name for the llms.txt header."
      },
      "key_pages": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "url",
            "description"
          ],
          "properties": {
            "url": {
              "type": "string",
              "description": "Absolute URL of the page."
            },
            "description": {
              "type": "string",
              "description": "One line describing what the page covers."
            }
          }
        },
        "description": "Pages worth pointing an AI system to, most important first."
      },
      "description": {
        "type": "string",
        "description": "A one-sentence summary of what the site is."
      }
    }
  }

  arguments 38 lines copy
• get_playbook unknown never probed

  Returns the full GEO/AEO playbook for one of Citehound’s fourteen verticals, as plain readable text: the strategic shift, three actionable strategies, outdated pitfalls to avoid, and an expert-tip placeholder. Called with no argument it returns the list of the fourteen verticals with their track (B2B SaaS, consumer and e-commerce brands, local and independent professionals), so call it with no argument first when you do not know which vertical fits. Read from this project’s own content file, so it matches what a visitor to the site sees. Fetches nothing external. Use after scan_site to turn a failed check into the strategy that applies to the site’s category.

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  {
    "type": "object",
    "properties": {
      "vertical": {
        "type": "string",
        "description": "Optional. A vertical slug. B2B SaaS: crm, martech, hrtech, fintech, cybersecurity, devtools. Consumer brands: ecommerce, consumerapps, hospitality, marketplaces. Professionals: health, localservices, realestate, legal. Omit to list them all."
      }
    }
  }

  arguments 9 lines copy
• list_ai_crawlers unknown never probed

  Lists the 10 AI crawlers Citehound tracks: which company runs each, what allowing or blocking it means, and this project’s robots.txt generator’s default for it. Use before generate_robots_txt if you need the exact tracked names. Fetches nothing.

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

  arguments 4 lines copy
• get_citation_prompts unknown never probed

  Returns the set of buying-intent questions Citehound uses to test whether an AI assistant names a brand, for one vertical, plus a short protocol a person can run in their own assistant: ask each question in a separate conversation three to five times, note whether the brand is named and whether it appears in a heading, list label, table or bold text, and judge the spread rather than one answer. The questions don’t name any brand in the category being measured. A few mention an integration platform such as Slack or Google Workspace, because that is how buyers ask. Read-only: it reads a content file and queries no model. There is deliberately no tool that runs the check for you: a run is about 90 model calls at about five seconds each, far past this server’s time limit, and it would spend a quota anyone could drain. Question sets exist for: consumerapps, crm, cybersecurity, devtools, ecommerce, fintech, health, hospitality, hrtech, legal, localservices, marketplaces, martech, realestate. Use when the user asks how to check whether an assistant names their brand, or wants the questions for their category.

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  {
    "type": "object",
    "required": [
      "vertical"
    ],
    "properties": {
      "vertical": {
        "type": "string",
        "description": "A vertical slug. Valid: consumerapps, crm, cybersecurity, devtools, ecommerce, fintech, health, hospitality, hrtech, legal, localservices, marketplaces, martech, realestate. Question sets exist for: consumerapps, crm, cybersecurity, devtools, ecommerce, fintech, health, hospitality, hrtech, legal, localservices, marketplaces, martech, realestate."
      }
    }
  }

  arguments 12 lines copy
• get_methodology unknown never probed

  Explains how the Citehound AI readiness score is built, read from the scanner’s own check registry so it matches what runs. With no argument it returns the three pillars with their weights (they add up to 100), every check with its category, points, what it tests and why it matters, and the limits of the scan: it measures readiness and not whether any assistant names a brand; it gives half credit for a crawler with any applicable Disallow rule, usually an ordinary path; and it reads the homepage only. With a check label it returns that one check in detail. An unknown label returns an error listing the valid labels. Read-only, fetches nothing external. Use when the user asks how a score is calculated, why a check carries the points it does, or what the scan cannot tell them.

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  {
    "type": "object",
    "properties": {
      "check": {
        "type": "string",
        "description": "Optional. A check label exactly as scan_site reports it, e.g. \"Page title\" or \"AI crawler access\". Case does not matter. Omit for the whole methodology."
      }
    }
  }

  arguments 9 lines copy
• get_sample_report unknown never probed

  Returns the sample Pro report as readable text: a real crawl of Citehound’s own site on a stated date, with the results shown as recorded. It gives the whole-site score beside the homepage score, the pillar averages, the priorities from the executive summary with the label saying what wrote it, the checks failing by number of pages, and the link to the full report, which also lists every page alphabetically and gives an estimated score if the fixes are applied. It shows what a full-site crawl adds to a one-page scan. It is our own site, not a customer’s, and it measures readiness, not whether any assistant names the site. Read-only, fetches nothing external. Use when the user asks what a full-site report looks like or what a crawl shows that a homepage scan cannot.

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

  arguments 4 lines copy
• generate_robots_txt unknown 38m ago

  Builds a robots.txt file that explicitly allows or blocks named AI crawlers from Citehound’s tracked list of 10, plus an optional Sitemap line. Pure text generation — fetches nothing. Call list_ai_crawlers first if you need the exact tracked crawler names.

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  {
    "type": "object",
    "properties": {
      "allow": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Crawler names to explicitly allow, e.g. [\"GPTBot\", \"ClaudeBot\"]. Must be names from the tracked list."
      },
      "block": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Crawler names to explicitly block. Must be names from the tracked list, and must not overlap with \"allow\"."
      },
      "sitemap_url": {
        "type": "string",
        "description": "Optional absolute sitemap URL to add as a Sitemap: line, e.g. \"https://example.com/sitemap.xml\"."
      }
    }
  }

  arguments 23 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.