AnswerRail AEO, Schema.org & /llms.txt Audit MCP
6-category AI readiness, Schema.org, robots.txt, /llms.txt & 25k-token audit + auto-fix MCP server
사용해야 할까요
품질 및 안전성
발견 사항 (5)
- HIGH
- LOWaudit_ai_readiness에서
- LOWgenerate_ai_manifests에서
- LOWextract_byte0_markdown에서
- LOWquery_truncation_benchmark에서
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"answerrail-mcp": {
"command": "npx",
"args": [
"answerrail-mcp"
]
}
}
}실행 가능한 패키지
1.2.7stdio원격 엔드포인트
https://answerrail.com/mcpstreamable-http할 수 있는 일
도구 목록
도구 (4)
🟢audit_ai_readiness(url)
Audits a website URL or domain for AI crawler access (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot), 25,000-token HTML RAG truncation, /llms.txt presence, Schema.org JSON-LD entities, and Google Lighthouse 13.5 Agentic Browsing (/.well-known/ai-catalog.json ARD 1.0). Use this tool first when diagnosing why a site is omitted or misquoted by ChatGPT, Perplexity, or Claude. Do not use this tool to extract full-page Markdown content (use extract_byte0_markdown instead) or to generate static manifest files (use generate_ai_manifests instead). Read-only network check; requires no API key.
입력 스키마
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Fully qualified HTTPS URL or bare domain name to audit (e.g., \"https://stripe.com/pricing\" or \"example.com\"). Defaults to HTTPS if scheme is omitted."
}
},
"required": [
"url"
],
"additionalProperties": false
}🟢generate_ai_manifests(domain)
Compiles ready-to-save /llms.txt documentation index and Google Lighthouse 13.5 /.well-known/ai-catalog.json (ARD 1.0) JSON manifests from a live domain, plus HTML <head> and robots.txt Agentmap snippets. Use this tool when a developer wants to remediate missing discovery manifests identified by audit_ai_readiness. Do not use this tool for general web scraping (use extract_byte0_markdown instead). Read-only synthesis; requires no API key and does not modify remote servers.
입력 스키마
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Target domain name or root URL to generate manifests for (e.g., \"example.com\" or \"https://example.com\")."
}
},
"required": [
"domain"
],
"additionalProperties": false
}🟢extract_byte0_markdown(url)
Strips React/Next.js hydration scripts, Shopify Liquid/WordPress DOM bloat, and inline SVGs/CSS from any URL, returning clean Byte-0 GitHub-Flavored Markdown (GFM) with front-loaded Schema.org tables. Use this tool when you need to read a web page or pricing table inside an LLM context window without exceeding the 25,000-token truncation limit. Do not use this tool if you only need a diagnostic readiness score or robots.txt check (use audit_ai_readiness instead). Read-only HTTP extraction; requires no API key.
입력 스키마
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Fully qualified URL of the web page to convert into Byte-0 Markdown (e.g., \"https://vercel.com/pricing\")."
}
},
"required": [
"url"
],
"additionalProperties": false
}🟢query_truncation_benchmark(domain)
Retrieves empirical benchmark telemetry from the 2026 50-Site SaaS & E-Commerce AI Truncation Study (measuring raw HTML token weight, 25,000-token Perplexity/ChatGPT cutoff truncation, visible price omission, /llms.txt adoption, and Lighthouse 13.5 ARD 1.0 adoption). Use this tool when comparing a site against industry peers (such as Stripe, Vercel, Datadog, or Shopify) or answering research questions about the Static /llms.txt Paradox. Do not use this tool to run a live scan on an arbitrary unbenchmarked URL (use audit_ai_readiness instead). Read-only benchmark lookup; requires no API key.
입력 스키마
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Optional domain name (e.g., \"stripe.com\" or \"vercel.com\") to filter the benchmark study. Omit or pass empty string to return the full 50-site statistical summary."
}
},
"additionalProperties": false
}커뮤니티
증거