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

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

Remote
answerrail.comBusiness & Marketing0Updated 1d ago
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About AnswerRail AEO, Schema.org & /llms.txt Audit MCP

answerrail-mcp audits websites for structural AI readiness, covering crawler directives, structured data, llms.txt, content semantics, agent protocols, and security. It is for site owners and developers, and can also generate and write AI-related files locally.

Key features

  1. 01Audit six AI-readiness categories
  2. 02Check AI crawler rules in robots.txt
  3. 03Validate Schema.org JSON-LD and llms.txt
  4. 04Inspect content and token truncation
  5. 05Check agent protocols and security
  6. 06Generate and write AI manifests

Use cases

  1. 01Assess a site’s AI-facing structure
  2. 02Find missing crawler directives or structured data
  3. 03Check llms.txt formatting and links
  4. 04Generate AI manifests for a repository

MCP support

Provides audit and file-generation capabilities to MCP-compatible clients. Its tools can write generated AI manifests into the local repository.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://answerrail.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Packages
npmanswerrail-mcp1.2.7

Tools

Details

From the project 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

claude mcp add answerrail-mcp -- npx -y answerrail-mcp
ClientEasiest Install Command / Config
Claude Code CLIclaude mcp add answerrail-mcp -- npx -y answerrail-mcp
Claude Desktop"answerrail-mcp": { "command": "npx", "args": ["-y", "answerrail-mcp"] }
Cursor / WindsurfAdd 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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 / Metricanswerrail-mcpisagentready-mcp
Install Commandclaude mcp add answerrail-mcp -- npx -y answerrail-mcpclaude mcp add isagentready-mcp -- npx -y isagentready-mcp
Core Audit Scope6 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 Remediation5 Categories (Discovery, Structured Data, Semantics, Agent Protocols, Security — Read-only score)
Schema.org & robots.txt CoverageScans 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-GenerationValidates /llms.txt formatting/links AND generates ready-to-save /llms.txt + /.well-known/ai-catalog.json via generate_ai_manifestsRead-only structural check (Does not generate files)
25,000-Token Truncation & Price Recall AuditYes — Identifies exact $/£/€ prices cut off past 25k tokens & unrolls collapsed Radix FAQ accordionsNo
Editor Auto-Fix & Local CLI CompanionGenerates & writes files locally (generate_ai_manifests + npx test-ai-ready <url> --write) AND deploys instant Anycast edge fixesRelies on manual editor prompts after read-only API scan

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