Skip to main content
Glama
ONE8943

ai-furniture-hub

by ONE8943
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Related Servers

Alternatives to ai-furniture-hub

No user-submitted related servers found.

    Related Servers

    • rakuten-japan-mcp

      atushi1841
      F
      license
      Not graded
      quality
      B
      maintenance
      MCP server to search Rakuten Ichiba (Japan's largest e-commerce platform) for products, returning names, prices, URLs, shop info, reviews, and images via the official Rakuten API.
      -

    • Doorzo Deal Monitor

      TESTYEE-09
      A
      license
      A
      quality
      C
      maintenance
      MCP server for finding and monitoring deals on Japanese shopping sites via Doorzo. It enables AI assistants to search across 9 marketplaces, set price alerts, and check for new matches.
      7
      MIT

    • chatbuy-mcp

      yukihamada
      F
      license
      A
      quality
      C
      maintenance
      A local AI shopping MCP server for Amazon.co.jp that searches products, adds them to cart, and opens the review page without automating purchase confirmation. It keeps your Amazon login confined to your machine's Chrome profile.
      5
      -

    • KaliCart Global

      giuseppesocci-bot
      F
      license
      Not graded
      quality
      C
      maintenance
      Federated commerce search MCP server enabling AI agents to query real product offers, prices, and availability across independent WooCommerce stores without API keys or registration.
      -

    • VectorCart Shopping Assistant

      Rounit-1st
      A
      license
      Not graded
      quality
      B
      maintenance
      MCP server that enables AI shopping assistants to understand customer needs, search products using visual and semantic queries, apply metadata filters, and recommend relevant products from an inventory.
      Apache 2.0
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4/5.0

    Scored across 15 tools

    Disambiguation3/5

    The tools have distinct primary purposes, but there is notable overlap in search and recommendation functions. For example, search_products, search_rakuten_products, and search_amazon_products all handle product searches across different platforms, while get_popular_products and get_curated_sets both provide recommendations, which could cause confusion in tool selection. Descriptions help clarify, but the boundaries are not always sharp.

    Naming Consistency4/5

    Most tools follow a consistent verb_noun naming pattern (e.g., calc_room_layout, compare_products, get_product_detail), which aids predictability. However, there are minor deviations like diagnose_ai_visibility and identify_product, which use different verb styles or compound terms, slightly breaking the pattern but remaining readable overall.

    Tool Count5/5

    With 15 tools, the server is well-scoped for its furniture and home goods domain, covering aspects like layout calculation, product search, comparison, recommendations, and diagnostics. Each tool appears to serve a specific function without redundancy, making the count appropriate for the intended purpose.

    Completeness4/5

    The toolset provides comprehensive coverage for furniture and home goods, including search, comparison, recommendations, layout planning, and product identification. Minor gaps exist, such as no explicit tools for user account management or order tracking, but these are not core to the domain, and agents can work around them with the available tools.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources