maxaeo-ai-visibility-mcp
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Alternatives to maxaeo-ai-visibility-mcp
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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- AlicenseAqualityCmaintenanceEnables AI agents to audit and improve a site's visibility in AI search by checking robots.txt against 17 major AI crawlers, detecting llms.txt, and returning a 0-100 visibility score with per-crawler verdicts; it also lists known AI crawlers and their purposes.2MIT
- 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.421 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents like Claude and Cursor to audit websites for AI agent readiness, checking 199 rules across agentic discovery, content structure, and technical SEO.17Apache 2.0
- 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
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to create, validate, and audit llms.txt files for websites, including checking against Google Lighthouse's Agentic Browsing requirements, via Streamable HTTP with no API key needed.7 npm2MIT
TDQS
Scored across 3 tools
The three tools have largely distinct focuses: check_llms_txt is specifically for llms.txt validation, audit_ai_crawler_readiness covers robots and metadata checks, and build_ai_visibility_report produces an overall report. There is some conceptual overlap in robots/sitemap checks between the first two, but the descriptions are clear enough to guide selection.
All tool names follow a consistent verb_noun pattern: check_llms_txt, audit_ai_crawler_readiness, build_ai_visibility_report. The naming style is uniform and predictable.
With three tools, the server is on the minimal side but still within a reasonable scope for an AI visibility audit tool. Each tool covers a distinct phase of the audit workflow, so the count feels purposeful rather than padded.
The server covers the core audit lifecycle: llms.txt validation, crawler readiness checks, and a combined visibility report. Minor gaps exist, such as no dedicated tool for viewing raw history or fixing identified issues, but these are not required for the stated audit purpose.