maxaeo-ai-visibility-mcp
Related Servers
Alternatives to maxaeo-ai-visibility-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceEnables inspection of any website's AI-search readiness, checking AI crawler blocks, llms.txt, schema markup, and indexing directives from MCP clients like Claude.428MIT
- 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 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.82MIT
- FlicenseNot gradedqualityBmaintenanceGenerates a complete suite of AI readiness files (llms.txt, ai.txt, schema, RAG indexes) for any website to optimize representation in ChatGPT, Claude, Gemini, and Perplexity.3-
- AlicenseNot gradedqualityAmaintenanceAudits public websites for AI crawler access, public technical signals, and deployment readiness. Includes a focused path to the full readiness report when deeper remediation guidance is needed.MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to perform instant SEO audits, check robots.txt, sitemaps, and AI crawler access for any URL without API keys.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.