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 inspection of any website's AI-search readiness, checking AI crawler blocks, llms.txt, schema markup, and indexing directives from MCP clients like Claude.434 npmMIT
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- 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.11 npm2MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to crawl live websites, audit AEO readiness, generate Schema.org @graph JSON-LD, llms.txt, ai.txt, and robots.txt, inject structured data into HTML, validate optimizations, and retrieve framework-specific code snippets.MIT
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.