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Alternatives to aivis-mcp

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    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Checks what GPTBot, ClaudeBot, PerplexityBot and 50+ other AI crawlers can read on a site and what its access policy allows, checks whether a page permits search, answers, training or agent use (with a signed receipt), generates llms.txt, robots.txt and JSON-LD, and reads your Answer Analysis. Nine tools for Claude Desktop, Cursor and other MCP clients.
      9
      460 npm
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables MCP clients to check whether AI answer engines such as ChatGPT, Claude and Perplexity can read and cite a given site, reporting robots.txt rules, edge-level bot blocks, llms.txt, structured data, JS-only content and other citation blockers with a score and fixes. It can also draft a starter llms.txt file from a site's homepage.
      2
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables users to audit websites and online stores for generative and answer-engine readiness, generate deployable llms.txt and llms-full.txt files, JSON-LD schema, and AI crawler rules, analyze robots access for AI bots, and extract clean page content for LLM use.
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Enables MCP clients to audit AI search visibility, generate intent-aligned social content, and assess Agentic Commerce Readiness with remediation files.
      3
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables auditing websites for AI/LLM readiness and generating fixes such as robots.txt rules, llms.txt, JSON-LD schema, and meta tags, plus checking brand and domain citations through natural language.
      1
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      MCP server for auditing product pages to determine AI shopping agent readiness, providing a 0–100 score and actionable fixes.
      11
      5
      Apache 2.0

    TDQS

    A4.3/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools target completely different activities: one performs a technical visibility audit (robots.txt, raw HTML, agent-commerce layer), while the other generates prompts and a rubric for recommendation testing. Their descriptions make the distinction unambiguous, so an agent can easily pick the right tool.

    Naming Consistency5/5

    Both tool names follow a clear verb_noun pattern in snake_case (check_ai_visibility, build_recommendation_test). The naming is consistent and predictable, with no deviations in style or convention.

    Tool Count3/5

    The server has only 2 tools, which feels thin even for a focused audit service. While each tool is substantive, the surface lacks depth—for example, no tool to run or score the recommendation test, or to track changes over time—making the set borderline.

    Completeness4/5

    The tools cover the core diagnostic tasks: technical AI visibility checks and prompt generation for recommendation testing. There are minor gaps, such as no tool to apply the generated rubric or to store historical results, but these can be worked around by the calling assistant or user.

    Maintenance

    ActivityStale
    ResponsivenessNo recent community issues