AnswerRail MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| audit_local_codebase_and_generateB | Performs a deep local filesystem structural compliance audit across 6 weighted categories (scanning local robots.txt against 14+ AI crawler directives, scanning local HTML/TSX/JSX files across 20+ Schema.org JSON-LD entity types, validating /llms.txt syntax, and checking ARD 1.0 /.well-known/ai-catalog.json) and actively generates compliant /llms.txt and /.well-known/ai-catalog.json files in the target project directory. |
| audit_ai_readinessA | 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. |
| generate_ai_manifestsA | 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 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. |
| extract_byte0_markdownA | 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. |
| query_truncation_benchmarkA | 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| audit-website-ai-readiness | Run a full 25,000-token RAG truncation, /llms.txt, and Google Lighthouse 13.5 ard-schema (/.well-known/ai-catalog.json) audit on a target URL. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| 2026 SaaS 50-Site AI Truncation & /llms.txt Benchmark | Empirical study of 50 production SaaS & e-commerce pricing pages under PerplexityBot/1.0 (25k-token cutoff, visible price omission & Lighthouse 13.5 ARD 1.0 adoption). |
| AnswerRail Official /llms.txt Documentation Index | Root AI documentation manifest for AnswerRail. |
TDQS
Scored across 5 tools
The five tools mostly target distinct actions: local audit+generation, remote readiness audit, Markdown extraction, manifest synthesis, and benchmark lookup. The main overlap is between audit_local_codebase_and_generate and generate_ai_manifests, since both produce /llms.txt and ai-catalog.json artifacts; the descriptions differentiate local vs. live-domain generation, but an agent could still hesitate between them.
All names use snake_case and begin with a verb, producing a predictable verb_noun pattern overall. audit_local_codebase_and_generate is a long compound action with an embedded conjunction, and extract_byte0_markdown embeds a brand token, which are minor deviations from the cleaner pattern used by the other tools.
Five tools is well within a focused, useful range for an AI-readiness auditing server. Each tool covers a distinct stage of the workflow: local audit, remote diagnosis, content extraction, manifest generation, and benchmark comparison.
The surface covers the core lifecycle: diagnose readiness, extract clean content, generate remediation manifests, audit local files, and compare against benchmarks. Minor gaps include no batch/multi-URL comparison tool and no explicit post-generation validation for remotely deployed manifests, but agents can work around these with the existing tools.