Growthr SEO + GEO MCP
SEO + GEO tools in your AI editor: scan as ChatGPT and Google crawlers see it, fix order, llms.txt.
我該用這個嗎
品質與安全性
發現項目（3）
- HIGHTool poisoning patterns detected
- MEDIUMTool 'growthr_llms_txt' description contains placeholder text在 growthr_llms_txt 中
- INFOTool description contains placeholder or incomplete text在 growthr_llms_txt 中
根據工具定義與協定合規性的自動化分析。
上下文成本
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": {
"seo-geo-mcp": {
"url": "https://mcp.growthr.com/mcp"
}
}
}遠端端點
https://mcp.growthr.com/mcpstreamable-http它能做什麼
工具清單
工具（4）
🟢growthr_scan(domain)
Fetch a public domain the way search engines and AI crawlers do (ChatGPT's GPTBot, Claude's ClaudeBot, and the fetchers behind Perplexity and Google AI Overviews: from a datacenter IP, no JavaScript) and run 22 weighted checks: reachability as a browser, GPTBot, and ClaudeBot; server-rendered content; metadata; strict JSON-LD and Organization schema; robots.txt access for AI crawlers; llms.txt and an in-page link to it; sitemap; honest 404s; markdown negotiation; trust pages; speed; and on-page SEO (title, description, single h1, viewport, alt text, favicon). Returns a 0-100 score and every check with pass/fail, what was found, and a fix hint. Reads about a dozen public URLs once. Growthr logs the domain and tool name for usage stats.
輸入結構描述
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Public domain, e.g. example.com (no path)"
}
},
"required": [
"domain"
]
}輸出結構描述
{
"type": "object",
"properties": {
"domain": {
"type": "string"
},
"score": {
"type": "integer",
"description": "0-100"
},
"checks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"label": {
"type": "string"
},
"pass": {
"type": "boolean"
},
"detail": {
"type": "string"
},
"weight": {
"type": "number"
}
},
"required": [
"id",
"label",
"pass"
]
}
},
"throttledCount": {
"type": "integer",
"description": "Requests the site answered with HTTP 429"
}
},
"required": [
"domain",
"score",
"checks"
]
}🟢growthr_llms_txt(domain)
Read a site's homepage, sitemap (or homepage links), and up to twelve pages, then draft an llms.txt in the llmstxt.org format: H1 name, blockquote summary, grouped page list with one-line descriptions, contact, profiles, links. The 'When to use' section is left as a marked placeholder on purpose; it is a judgment about the business that no crawler can write. Returns the draft plus notes on anything skipped.
輸入結構描述
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Public domain, e.g. example.com"
}
},
"required": [
"domain"
]
}輸出結構描述
{
"type": "object",
"properties": {
"domain": {
"type": "string"
},
"pages": {
"type": "integer",
"description": "Pages read"
},
"notes": {
"type": "array",
"items": {
"type": "string"
}
},
"llmsTxt": {
"type": "string",
"description": "The draft, llmstxt.org format"
}
},
"required": [
"domain",
"llmsTxt"
]
}🟢growthr_fix_order(domain)
Run the scan and sort every failed check into buckets: blockers (fix before anything else, they hide everything downstream), this afternoon (metadata, llms.txt, trust pages, on-page), needs a sprint (schema, markdown negotiation, speed), and the off-site work no scan can measure (reviews, directories, third-party mentions), which is what decides whether AI engines recommend a business.
輸入結構描述
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Public domain, e.g. example.com"
}
},
"required": [
"domain"
]
}輸出結構描述
{
"type": "object",
"properties": {
"domain": {
"type": "string"
},
"score": {
"type": "integer"
},
"blockers": {
"type": "array",
"items": {
"type": "string"
},
"description": "Check ids to fix before anything else"
},
"afternoon": {
"type": "array",
"items": {
"type": "string"
}
},
"sprint": {
"type": "array",
"items": {
"type": "string"
}
},
"other": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"domain",
"score",
"blockers",
"afternoon",
"sprint"
]
}🟢growthr_ai_visibility(prompt, brand, domain)
Run one buyer-shaped prompt through Gemini with Google Search grounding (a real, cited web search, the same mechanism behind Google AI Overviews) and report where the brand lands on the five-rung ladder: absent, cited (a page of the brand's site is a source), mentioned (named in the text), recommended (on the shortlist), or recommended against. Also returns the other names on the shortlist and the source domains the answer was built from. One prompt per call; limited to a few calls per day per user because grounded requests are billed per query.
輸入結構描述
{
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The question a buyer would ask, e.g. 'best corporate event photographer in New York'"
},
"brand": {
"type": "string",
"description": "Brand name to look for, e.g. 'Growthr'"
},
"domain": {
"type": "string",
"description": "Optional brand domain, e.g. growthr.com, to detect citations"
}
},
"required": [
"prompt",
"brand"
]
}輸出結構描述
{
"type": "object",
"properties": {
"rung": {
"type": "string",
"enum": [
"absent",
"cited",
"mentioned",
"recommended",
"recommended against"
]
},
"cited": {
"type": "boolean"
},
"mentioned": {
"type": "boolean"
},
"recommended": {
"type": "boolean"
},
"against": {
"type": "boolean"
},
"shortlist": {
"type": "array",
"items": {
"type": "string"
},
"description": "Providers the answer put forward"
},
"shortlistSource": {
"type": "string",
"enum": [
"llm",
"heuristic"
]
},
"sources": {
"type": "array",
"items": {
"type": "string"
},
"description": "Source domains the answer was built from"
},
"engine": {
"type": "string"
}
},
"required": [
"rung",
"shortlist",
"sources",
"engine"
]
}社群
證據