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

io.github.quitegoodproject由 GitHub 账号 quitegoodproject 发布v1.2.7更新于 Oct 29, 2026

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

发布方自己的服务：audit.domains - Domain Valuation

已验证Streamable HTTP可网页运行网页搜索与抓取开发者工具

服务详情

标识符: io.github.quitegoodproject/answerrail-mcp
传输: Streamable HTTP
运行时: Web executable
信任: Verified
发布于: Oct 29, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 quitegoodproject。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![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)

概览

AI 生成的概览

审计网站的 AI 就绪度（robots.txt、Schema.org、/llms.txt、token 截断），并生成缺失的发现清单文件。

可以做什么: 按爬虫指令、Schema.org JSON-LD、/llms.txt 结构、token 截断与代理协议审计网站 AI 就绪度
生成可直接保存的 /llms.txt 与 ai-catalog.json 清单，以及 head 与 robots.txt 片段
从任意网址提取干净的 Byte-0 Markdown，去除水合脚本与 DOM 冗余
获取 50 个站点的 AI 截断研究基准数据用于对比
适用场景: 适合在需要判断网站能否被 AI 爬虫与助手发现和读取时使用，或需要为自维护的站点补上缺失的 /llms.txt、ai-catalog.json 与 robots.txt 声明时使用。
运行要求: 可作为远程端点使用，也可通过 npx 以本地 npm 包运行；本地 stdio 安装需要 Node.js。未声明 API 密钥或环境变量。需要能访问被审计站点的网络。

安装前请注意

清单生成工具会产出要写入代码仓库的文件，提交前应检查生成内容。审计与页面提取会抓取第三方网址，这些网址会发送给服务器或被审计站点。

安装

在 SourceWeft 中

打开控制台中的 AnswerRail AEO, Schema.org & /llms.txt Audit MCP，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.quitegoodproject%2Fanswerrail-mcp/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "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

来源：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.

输入参数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.

输入参数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.

输入参数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.

输入参数domain