AnswerRail AEO, Schema.org & /llms.txt Audit MCP

io.github.quitegoodproject由 GitHub 帳號 quitegoodproject 發布v1.2.7更新於 Oct 29, 2026

6-category AI readiness, Schema.org, robots.txt, /llms.txt & 25k-token audit + auto-fix MCP server

發布方自己的服務：audit.domains - Domain Valuation

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取開發者工具

服務詳細資訊

識別碼: io.github.quitegoodproject/answerrail-mcp
傳輸: Streamable HTTP
執行環境: Web executable
信任: Verified
發布於: Oct 29, 2026

安全提示

MCP 服務會接收對話中的工具呼叫請求，並可能執行外部操作。啟用前請先了解該服務能做什麼，並只授予它所需的權限。

註冊中心已驗證此條目所屬的 GitHub 帳號 quitegoodproject。這並不表明該帳號就是產品方。

SourceWeft 的檢查只涵蓋條目是否完整、端點是否回應；不審查伺服器的程式碼，也不審查其工具的行為。

在 README 中新增徽章

向讀者展示該 MCP 服務已收錄於 SourceWeft，徽章連結到本頁。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/io.github.quitegoodproject%2Fanswerrail-mcp.svg)](https://sourceweft.com/mcp/io.github.quitegoodproject%2Fanswerrail-mcp?utm_source=readme&utm_medium=badge)

概覽

AI 產生的概覽

稽核網站的 AI 就緒度（robots.txt、Schema.org、/llms.txt、token 截斷），並產生缺少的探索清單檔案。

可以做什麼: 依爬蟲指令、Schema.org JSON-LD、/llms.txt 結構、token 截斷與代理協定稽核網站 AI 就緒度
產生可直接儲存的 /llms.txt 與 ai-catalog.json 清單，以及 head 與 robots.txt 片段
從任意網址擷取乾淨的 Byte-0 Markdown，去除水合腳本與 DOM 冗餘
取得 50 個網站的 AI 截斷研究基準資料以供比較
適用情境: 適合在需要判斷網站能否被 AI 爬蟲與助理探索與讀取時使用，或需要為自己維護的網站補上缺少的 /llms.txt、ai-catalog.json 與 robots.txt 宣告時使用。
執行需求: 可作為遠端端點使用，也可透過 npx 以本機 npm 套件執行；本機 stdio 安裝需要 Node.js。未宣告 API 金鑰或環境變數。需要能連線至被稽核網站的網路。

安裝前請注意

清單產生工具會產出要寫入程式碼儲存庫的檔案，提交前應檢查產生內容。稽核與頁面擷取會抓取第三方網址，這些網址會傳送給伺服器或被稽核網站。

安裝

在 SourceWeft 中

開啟儀表板中的 AnswerRail AEO, Schema.org & /llms.txt Audit MCP，將其新增到工作區。
為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.quitegoodproject%2Fanswerrail-mcp/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "answerrail-aeo-schema-org-llms-txt-audit": {
      "type": "http",
      "url": "https://answerrail.com/mcp"
    }
  }
}

README

answerrail-mcp — 6-Category Structural AI Readiness, Schema.org, `robots.txt` & `/llms.txt` Audit + Auto-Remediation MCP Server

[npm version] [License: MIT] [MCP]

Comprehensive Model Context Protocol (MCP) server for exhaustive local and live structural audits across 6 weighted AI readiness categories—plus active file generation inside Claude Code, Claude Desktop, Cursor, and Windsurf. No API key required.

Quick Install for Claude Code, Claude Desktop & Cursor

Client	Easiest Install Command / Config
Claude Code CLI	`claude mcp add answerrail-mcp -- npx -y answerrail-mcp`
Claude Desktop	`"answerrail-mcp": { "command": "npx", "args": ["-y", "answerrail-mcp"] }`
Cursor / Windsurf	Add `npx -y answerrail-mcp` in `.cursor/mcp.json`

Exhaustive 6-Category Weighted Structural Audit Scope

Unlike read-only 5-category scanners, answerrail-mcp performs a 6-category deep structural audit and actively writes the missing files into your repository:

AI Bot Directives (robots.txt — 14+ Major AI Crawlers + Agentmap:): Audits exact RFC 9309 allow/disallow rules for 14+ AI user-agents (PerplexityBot, OAI-SearchBot, GPTBot, ChatGPT-User, ClaudeBot, Claude-User, Google-Extended, Applebot-Extended, Meta-ExternalAgent, Amazonbot, DuckAssistBot, Cohere-ai, Bytespider, CCBot) and verifies Agentmap: header/file declarations.
Search Signals & Structured Data (Schema.org JSON-LD — 20+ High-Value Entity Types): Scans and validates 20+ Schema.org types (Organization, SoftwareApplication, WebSite, Product, Offer, AggregateOffer, FAQPage, Question, Answer, TechArticle, Article, BreadcrumbList, HowTo, Service, LocalBusiness, Person, Dataset, WebPage, ItemList, Review) and synthesizes them into Byte-0 GitHub-Flavored Markdown tables.
Deep /llms.txt & /llms-full.txt Structural Validation: Audits H1 title conformance, blockquote summary presence, Markdown link formatting, HTTP Content-Type: text/markdown headers, and entry quality.
Content Semantics & 25,000-Token RAG Truncation: Measures raw HTML token weight, Next.js self.__next_f.push RSC hydration bloat, collapsed Radix/Headless UI (data-state="closed") FAQ accordions, and exact $/£/€ pricing tokens dropped past the 25,000-token cutoff.
Agent Protocols (Google Lighthouse 13.5 ARD 1.0 & WebMCP): Validates /.well-known/ai-catalog.json (specVersion: "1.0"), <link rel="ai-catalog">, <link rel="describedby" href="/llms.txt">, and /.well-known/mcp.json.
Security & Cryptographic Provenance (RFC 9421 & RFC 9110): Audits asymmetric Ed25519 HTTP Message Signatures (kid="ar_ed25519_2026_k1"), /.well-known/http-message-signatures-directory, CORS (Access-Control-Allow-Origin: *), and RFC 9110 Vary: User-Agent, Accept compliance.

Head-to-Head Comparison: `answerrail-mcp` vs `isagentready-mcp`

Feature / Metric	`answerrail-mcp`	`isagentready-mcp`
Install Command	`claude mcp add answerrail-mcp -- npx -y answerrail-mcp`	`claude mcp add isagentready-mcp -- npx -y isagentready-mcp`
Core Audit Scope	6 Weighted Categories: AI Bot Directives (`robots.txt`), Search Signals (`Schema.org JSON-LD`), Deep `/llms.txt` Structure, Content Semantics & 25k-Token Truncation, Agent Protocols (ARD 1.0 / WebMCP), and Security (RFC 9421 Ed25519) + Edge Remediation	5 Categories (Discovery, Structured Data, Semantics, Agent Protocols, Security — Read-only score)
Schema.org & `robots.txt` Coverage	Scans 20+ Schema.org JSON-LD types & 14+ AI bot rules in `robots.txt` (plus compiles JSON-LD into Byte-0 GFM tables)	Scans 17 Schema.org types & 13 AI bot rules
`/llms.txt` Validation & Auto-Generation	Validates `/llms.txt` formatting/links AND generates ready-to-save `/llms.txt` + `/.well-known/ai-catalog.json` via `generate_ai_manifests`	Read-only structural check (Does not generate files)
25,000-Token Truncation & Price Recall Audit	Yes — Identifies exact `$/£/€` prices cut off past 25k tokens & unrolls collapsed Radix FAQ accordions	No
Editor Auto-Fix & Local CLI Companion	Generates & writes files locally (`generate_ai_manifests` + `npx test-ai-ready <url> --write`) AND deploys instant Anycast edge fixes	Relies on manual editor prompts after read-only API scan

來源：README.md，提交 3e62aca

工具

audit_ai_readiness

Audit Website AI Crawler & Lighthouse 13.5 Readiness

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.

輸入參數url必填

generate_ai_manifests

Generate /llms.txt & Lighthouse 13.5 ai-catalog.json Manifests

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.

輸入參數domain必填

extract_byte0_markdown

Extract Clean Byte-0 Markdown & Schema Tables from 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.

輸入參數url必填

query_truncation_benchmark

Query 2026 50-Site SaaS AI Truncation & /llms.txt Benchmark

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.

輸入參數domain