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AI Furniture Hub

by ONE8943

Searches Japanese furniture and home products by dimensions, compares options, builds room sets, and checks AI visibility.

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Install AI Furniture Hub

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "one8943-ai-furniture-hub": {
      "command": "npx",
      "args": [
        "-y",
        "ai-furniture-hub"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y ai-furniture-hub

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

The ONE8943/ai-furniture-hub MCP server gives AI agents structured access to Japan-focused furniture and home-product data. It exposes 15 tools and three prompts for millimeter-based product search, room planning, product comparison, curated sets, replacements, and AI visibility audits. Local stdio execution works through npm, while a hosted Streamable HTTP endpoint is available for compatible clients. Reach for it when recommendations depend on exact dimensions, related accessories, Japanese marketplace results, or room-level purchasing plans.

Use cases

  • Find furniture that fits a measured space
  • Build room-specific shopping checklists
  • Compare products and their running accessories
  • Locate dimension-compatible replacements
  • Audit a website for AI discoverability

Key features

  • Millimeter-based and rotation-aware product search
  • Rakuten search and Amazon affiliate URL generation
  • Room layout and storage coordination
  • Curated bundles, presets, and influencer picks
  • Dimension-based replacement fit scores
  • AI visibility audits with structured recommendations

Tools15

What AI Furniture Hub lets your agent do. Expand a tool for its parameters.

Self-reported~318 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10d ago via OSV.dev · ai-furniture-hub (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1Stargazers on the source repository.
npm downloads342Package downloads in the last 30 days.
Last commit6mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Tools exposed15Callable tools this server registers over MCP.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How AI Furniture Hub works

What ONE8943/ai-furniture-hub MCP server does

The ONE8943/ai-furniture-hub MCP server helps AI agents search and reason about furniture, storage, appliances, accessories, and other home products for the Japanese market. Its local dataset contains more than 355 curated products across 31 categories and 90-plus brands. Product records can include outer and inner dimensions, prices, colors, brands, load information, compatible storage, consumables, and related items.

The central use case is space-constrained shopping. An agent can search for an item that fits a specified width, depth, or height in millimeters, including cases where rotating the product changes whether it fits. The server also supports coordinated recommendations, such as pairing a shelf with storage boxes, floor protection, cable organizers, or other accessories.

Other workflows cover room layouts, discontinued-product replacements, curated room configurations, influencer selections, budget-oriented hack sets, and product comparisons. The visibility-audit tool evaluates a website’s AI discoverability rather than its furniture catalog.

How it works

MCP clients can connect over stdio or Streamable HTTP. The hosted endpoint is https://ai-furniture-hub.onrender.com/mcp, and the project also publishes a local npm runner. The server exposes 15 tools and three prompts. Tools handle product discovery, detailed records, marketplace searches, comparisons, layout calculations, category browsing, curation, and visibility diagnosis.

Local responses can use mock data, so external API credentials are not required for the basic setup described in the project materials. Marketplace-specific functions include live Rakuten Ichiba search and Amazon affiliate search URL generation. The README also identifies Nitori as an adapter in the architecture, but does not provide a separate Nitori tool in the listed tool table.

Responses include _attribution metadata with an attribution_id. The project describes this as support for tracking request sources and revenue attribution. It also exposes MCP resources and machine-readable discovery files, including llms.txt, full tool schemas, an OpenAPI 3.1 document, and server metadata.

Setup and configuration

For local stdio use, run npx ai-furniture-hub. The repository also supports cloning the project, installing its npm dependencies, and starting it in either stdio or HTTP mode. The local HTTP mode serves MCP at localhost:3000/mcp.

A client that accepts remote Streamable HTTP can use the hosted endpoint directly. The README gives examples for Cursor, Claude, VS Code, and ChatGPT-compatible remote MCP configurations. The MCP Registry identifier is io.github.ONE8943/ai-furniture-hub.

The documented environment settings include DEPLOYMENT_MODE, which selects private or public deployment behavior, and MCP_API_KEYS_FREE for configuring free-tier keys.

Tools and capabilities

The ONE8943/ai-furniture-hub MCP server groups its tools into several practical areas:

  • Search products by keyword, dimensions, price, color, category, or brand.
  • Retrieve detailed specifications and related product information.
  • Search Rakuten listings or generate Amazon affiliate search URLs.
  • Find products that fit a defined space and account for rotation.
  • Compare two to five products by price, dimensions, load, and reviews.
  • Locate replacements and rank dimension-compatible alternatives with a fit_score from 0 to 100.
  • Calculate room layouts using rectangular floor-plan packing and placement coordinates.
  • Build storage combinations, curated sets, room presets, influencer picks, and budget alternatives.
  • Diagnose website AI visibility using checks for llms.txt, robots rules, JSON-LD, OGP metadata, and related readiness signals.

The three prompts support room coordination, moving checklists, and product showdowns. These workflows turn several tool calls into structured purchasing or comparison tasks.

Limitations and notes

The catalog is Japan-focused, and live marketplace coverage depends on the relevant adapter or API. Live Rakuten results and attribution behavior may also differ from the mock-data path.

The project publishes both an OpenAPI schema and MCP discovery resources, but the listing does not describe a separate authenticated REST deployment. It also does not state that a paid API key is mandatory. Use the hosted endpoint or npm command according to the client’s MCP transport support, and verify marketplace credentials separately if a live search does not work without them.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about AI Furniture Hub

Run `npx ai-furniture-hub` for local stdio mode, or connect an MCP client to the hosted endpoint at `https://ai-furniture-hub.onrender.com/mcp`.

It does not provide a Claude Desktop-specific configuration block.

Yes. Product search and space suggestions use millimeter dimensions, check outer and inner measurements, and can account for rotating an item.

The provided setup says API keys are optional and that mock data works without them. Optional settings include `DEPLOYMENT_MODE` and `MCP_API_KEYS_FREE`.

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