answerbeacon
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- AlicenseNot gradedqualityCmaintenanceEnables auditing any URL for AEO and AI-search readiness, including direct-answer citability, JSON-LD validity, llms.txt compliance, E-E-A-T signals, agentic crawlability, citation simulation, and competitor score comparisons.95 npmMIT
- AlicenseAqualityBmaintenanceEnables auditing URLs for AI crawler readiness, including 25,000-token RAG truncation, /llms.txt discovery, Lighthouse 13.5 ard-schema compliance, and JSON-LD entities, plus generating manifests and clean Byte-0 Markdown inside MCP clients.5195 npmMIT
- AlicenseAqualityCmaintenanceEnables inspection of any website's AI-search readiness, checking AI crawler blocks, llms.txt, schema markup, and indexing directives from MCP clients like Claude.441 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables auditing any website for AI-answer readiness, returning a score and actionable fix list based on llms.txt, schema, sitemap, and related checks.MIT

agentbuiltofficial
AlicenseNot gradedqualityCmaintenanceEnables AI agents to run a free AI-readiness audit of any URL, checking AI crawler rules, JavaScript-free page text, JSON-LD, llms.txt, sitemap, meta description, FAQ schema, and returning a score with findings and fixes. Also exposes the same capability via an A2A agent endpoint.MIT- AlicenseNot gradedqualityCmaintenanceLets MCP clients audit any Shopify store mid-conversation for AI-search (GEO/AEO) readiness, returning scored results and paste-ready fixes as JSON. Runs the same local, signup-free pipeline as the CLI.55 npmMIT
TDQS
Scored across 2 tools
audit_page performs the scoring action on a URL, while explain_check only describes a single element's criteria; there is no plausible way to confuse the two. Each tool also has a distinct return shape (score plus evidence vs. explanatory text).
Both names use the same snake_case verb_noun pattern (audit_page, explain_check), with clear, concrete verbs. No mixed conventions or vague catch-all names.
Two tools is on the thin side, though they map cleanly to the only two actions implied by the domain (score a page, explain a criterion). The surface feels borderline minimal rather than over- or under-built for a single-purpose auditor.
The core lifecycle—run an audit and look up what each of the 20 checks means—is covered, and audit_page returns evidence plus fixes for every element, so there are few dead ends. A minor gap: there is no tool to enumerate e01...e20, so an agent must obtain IDs from audit output or guess them.