citable
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- 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 gradedqualityBmaintenanceEnables AI coding agents to score any live domain for visibility in AI answer engines and then generate and apply paste-ready fixes — robots.txt, llms.txt, JSON-LD, titles and meta — either directly to project files or as click-by-click admin steps for hosted platforms like Squarespace, Wix and Shopify. It also re-checks edits, reads server access logs to confirm which crawlers actually arrived and whether they were genuine, and offers hosted tools for citation testing, score history, drift and competitor comparison.40 npmMIT
- AlicenseAqualityAmaintenanceLets an MCP client check whether an ecommerce store can be read by AI assistants, auditing robots.txt to see which AI search versus training crawlers are blocked and sampling product pages to confirm structured data and facts survive in raw HTML without JavaScript. It can also generate a training-only opt-out robots.txt that leaves answer-engine crawlers allowed, all inside the conversation the user is already in.2MIT
- AlicenseAqualityAmaintenanceChecks whether ChatGPT, Claude, Perplexity, Google AI and 50+ AI crawlers can read a site, generates llms.txt, robots.txt and JSON-LD, reads LLM Monitor trends, and checks whether a page permits search, answers, training or agent use, with a signed receipt. Nine tools, from Claude Desktop, Cursor and other MCP clients.8206 npmMIT
- AlicenseAqualityDmaintenanceAn MCP server that gives AI agents tools to inspect a website's visibility to AI answer engines, including crawler permissions, llms.txt, structured data, on-page signals, and a full 29-check AI-readiness audit.50MIT
- AlicenseNot gradedqualityBmaintenanceEnables auditing AI search visibility: checks site readiness for AI crawlers and measures whether ChatGPT, Gemini, and Perplexity recommend your site, including verbatim answers and citation gap analysis.59 npm4AGPL 3.0
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: check_site audits AI crawler accessibility, while draft_llms_txt generates a starter file. Their names and descriptions make selection unambiguous.
Both names use consistent snake_case verb_noun structure: check_site and draft_llms_txt. The acronym in llms_txt is readable and does not break the pattern.
Two tools is thin for a site-auditing and remediation domain, even though each tool is focused. It fits a narrow MVP scope but leaves little room for related operations.
The surface covers checking AI readability and drafting /llms.txt, but lacks obvious companion operations like generating robots.txt fixes, producing sitemap guidance, or validating an existing llms.txt. Agents may hit dead ends when asked to remediate issues found by check_site.