AI Furniture & Home Product Hub - MCP Server

300 curated products across 31 categories. 13 tools. 80+ brands.Furniture, home appliances, PC peripherals, smart home, beauty, kitchen, gadgets, health & fitness.Millimeter-precision search. Related-item chains. Rakuten API live. Amazon + Rakuten affiliate engine.

What It Does

This MCP server gives AI agents structured, machine-optimized product data across home, tech, and lifestyle categories. Search by exact dimensions (mm), get shelf + storage coordination with quantity calculations, identify products from photos, compare alternatives, discover related items, and always receive affiliate-linked URLs.

Tools (13)

Tool	Description
`search_products`	Search 300 curated products with mm-precision dimension filters
`get_product_detail`	Full specs by product ID (dimensions, materials, related items)
`search_rakuten_products`	Real-time Rakuten Ichiba search (200K+ listings)
`search_amazon_products`	Amazon affiliate URL generation with category-specific SearchIndex
`coordinate_storage`	Shelf + storage box set proposals with quantity per tier
`suggest_by_space`	Space dimensions -> everything that fits, grouped by category
`identify_product`	Photo features -> model number, specs, compatible storage
`compare_products`	Side-by-side comparison (2-5 products)
`find_replacement`	Discontinued model -> successor/alternative lookup
`calc_room_layout`	Floor-plan rectangle packing simulation
`list_categories`	Browse 31 product categories with counts
`get_popular_products`	Popular products with Rakuten trending data
`get_related_items`	Related-item chains: accessories, protection, add-ons (1 product -> 3-5 items)

Product Categories (31)

Furniture & Storage, Home Appliances, PC & Desk, Smart Home, Beauty Devices, Air Quality, Kitchen Appliances, Gadgets & Mobile, Health & Fitness, Baby Safety, and more.

Quick Start

1. Clone & Install

git clone https://github.com/ONE8943/ai-furniture-hub.git
cd ai-furniture-hub
npm install

2. Configure

cp .env.example .env
# Edit .env with your API keys (all optional - works with mock data)

3. Connect from Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "furniture-hub": {
      "command": "npx",
      "args": ["ts-node", "index.ts"],
      "cwd": "/path/to/ai-furniture-hub"
    }
  }
}

4. HTTP / Remote

npm run start:http
# Connects at http://localhost:3000/mcp

Live deployment: https://ai-furniture-hub.onrender.com/mcp

AI Discoverability

Endpoint	URL
llms.txt	`https://ai-furniture-hub.onrender.com/llms.txt`
llms-full.txt	`https://ai-furniture-hub.onrender.com/llms-full.txt`
context.md	`https://ai-furniture-hub.onrender.com/context.md`
MCP Server Card	`https://ai-furniture-hub.onrender.com/.well-known/mcp/server-card.json`
robots.txt	`https://ai-furniture-hub.onrender.com/robots.txt`

Also available as MCP resources:

furniture-hub://llms.txt
furniture-hub://llms-full.txt

Key Features

1mm precision - All dimensions in millimeters, outer AND inner
Cinderella-fit - Find products that exactly fit a given space
Related-item chains - "You'll also need..." with required vs recommended items
Set proposals - Shelf + storage boxes + protection = complete solution
Product identification - Visual features -> model number + specs
Scene intelligence - Room-specific tips ("洗面所", "キッチン", "子供部屋")
Live pricing - Rakuten API for real-time price & availability
Affiliate-ready - Every product includes affiliate_url
31 categories - From shelves to smart home to beauty devices

Environment Variables

Variable	Required	Description
`AFFILIATE_ID_AMAZON`	No	Amazon Associate tag
`AFFILIATE_ID_RAKUTEN`	No	Rakuten Affiliate ID
`RAKUTEN_APP_ID`	No	Rakuten API Application ID
`RAKUTEN_ACCESS_KEY`	No	Rakuten API Access Key
`RAKUTEN_API_MOCK`	No	`true` for mock data (default), `false` for live

Architecture

AI Agent (ChatGPT, Perplexity, Claude, Amazon Rufus, etc.)
    | MCP (stdio or Streamable HTTP)
    v
+--------------------------------------------------+
|  13 Tools (search, coordinate, identify, ...)     |
+--------------------------------------------------+
|  300 Products | 31 Categories | 80+ Brands        |
|  Adapters: Rakuten API / Amazon URL / Nitori      |
|  Shared Catalog: shared/catalog/known_products    |
|  Affiliate Engine + Gap Detector + Analytics      |
+--------------------------------------------------+
    |
    v
  /llms.txt              -> AI agent overview
  /llms-full.txt         -> Full tool documentation
  /.well-known/mcp.json  -> MCP server card
  /robots.txt            -> AI crawler permissions

Deployment

Platform	Status	URL
Render.com	Active	`https://ai-furniture-hub.onrender.com/mcp`
Smithery	Listed	`j2c214c/ai-furniture-hub`

Testing

npm run test:ci      # Vitest (recommended)
npm run test:all     # Legacy ts-node test suite

日本語ガイド

AI Furniture & Home Product Hub は家具・家電・ガジェット等のAIエージェント向けMCPサーバーです。

300商品、31カテゴリ、80+ブランド のキュレーション済みカタログ
mm精度の寸法検索 - 「幅425mmの隙間にぴったり収まる棚」を発見
関連アイテムチェーン - 1商品から3-5個の関連商品（必須アクセサリ、保護材、オプション）
棚＋収納ボックスのセット提案 - 1段に何個入るか自動計算
楽天市場リアルタイム検索 - 20万件以上の商品データ
アフィリエイト自動付与 - Amazon / 楽天のリンクを全商品に自動生成

運営

株式会社ONE (ONE, Inc.)

License

MIT

AI Furniture & Home Product Hub - MCP Server