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What is this?

meshy-mcp is a Model Context Protocol server that connects Claude to the Meshy AI 3D generation API.

Once connected to Claude Desktop (or any MCP client), you can say:

"Generate a 3D model of a vintage leather messenger bag" "Turn this product photo into a 3D model" "Apply a carbon fiber texture to this model" "Build me a 3D showroom environment for my clothing brand"

…and Claude will call Meshy, poll until the model is ready, and hand you back download links for GLB, FBX, OBJ, and USDZ files — all without leaving the conversation.

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Quick Start

1. Get a Meshy API key

Sign up at app.meshy.ai → Settings → API Keys → Generate Key.

2. Add to Claude Desktop

Open your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "meshy-3d": {
      "command": "npx",
      "args": ["-y", "meshy-mcp"],
      "env": {
        "MESHY_API_KEY": "msy_your_key_here"
      }
    }
  }
}

Restart Claude Desktop. Done — Claude now has 3D generation tools.

See docs/setup.md for custom MCP clients, running from source, and troubleshooting.

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Tools

Six tools are exposed to Claude:

Tool	What it does
generate_3d_model	Text → 3D model (GLB, FBX, OBJ, USDZ)
refine_3d_model	Upgrade preview to production quality with PBR textures
generate_3d_from_image	Image URL → 3D model
add_texture	Apply AI-generated textures + PBR maps to any model
check_task_status	Poll status of any running task
list_3d_models	List recently generated models

Full parameter reference: docs/tools.md

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Demo prompts

Paste any of these into Claude after connecting meshy-mcp:

Generate a 3D model of a puffer jacket with a reflective quilted surface, realistic style, quad topology.

Turn this image into a 3D model and apply a woven denim texture: https://example.com/jacket.jpg

Generate a minimalist luxury showroom environment — white walls, concrete floor, warm spotlights — for a clothing brand website.

Generate a low-poly voxel sneaker for a game. Target 5000 polygons.

Create a 3D wristwatch model, then refine it to production quality with PBR textures.

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Use Cases

3D product visualization

Turn product descriptions or photos into 3D assets for e-commerce, AR try-on, and marketing. Works for fashion, furniture, electronics, and more.

3D website environments

Generate scene props and environments, export GLB files, and embed them with Three.js or Babylon.js. Build interactive 3D landing pages, virtual showrooms, and immersive brand experiences — all by describing them to Claude.

Print-on-demand & clothing brands

Generate photorealistic garment mockups, brand kit 3D assets, and complete lookbook scenes. Use alongside ThreadForge for the full clothing brand workflow.

Game & metaverse assets

Generate props, characters, and environment pieces with game-ready topology. Export FBX for Unreal Engine or Unity.

AR & spatial computing

USDZ output works with Apple AR Quick Look and visionOS. Generate and share AR-viewable 3D models directly from Claude.

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How Meshy built a $20M+ business on this API

Meshy is not just a tool — it's one of the most well-engineered AI data flywheels in the 3D space. Understanding how they built it changes how you think about using and building on top of their API.

The short version:

• The 4-variant limit per generation is not a cost-saving measure — it's a training data architecture decision
• Every model you pick over the others is a free preference signal (RLHF without asking)
• The free tier exists to collect data, not out of generosity
• After millions of generations, Meshy holds a (prompt → accepted output) dataset that no competitor can buy
• Every developer who builds on the API (like this repo) extends their distribution for free

Read the full breakdown: docs/how-meshy-built-a-3d-ai-empire.md

Covers: the technical stack, the data flywheel, the Midjourney parallel, pricing power risk, self-hosting alternatives, and the exact blueprint for replicating this in any vertical.

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Architecture

meshy-mcp/
├── src/
│   ├── index.ts           # Entry point
│   ├── server.ts          # MCP server + tool router
│   ├── meshy/
│   │   ├── client.ts      # Meshy REST API client
│   │   ├── types.ts       # Full TypeScript type definitions
│   │   └── poller.ts      # Async task completion poller
│   ├── tools/
│   │   ├── text-to-3d.ts  # generate_3d_model
│   │   ├── refine-3d.ts   # refine_3d_model
│   │   ├── image-to-3d.ts # generate_3d_from_image
│   │   ├── texture.ts     # add_texture
│   │   └── status.ts      # check_task_status + list_3d_models
│   └── utils/
│       └── format.ts      # Markdown output formatters
├── docs/                  # Setup + tool reference
├── examples/              # Claude Desktop config + prompt walkthroughs
├── CLAUDE.md              # AI agent context (for Claude Code)
└── .env.example

Protocol: stdio MCP (compatible with Claude Desktop, Cursor, Zed, and any MCP client) Language: TypeScript 5, ESM, Node.js 20+ Dependencies: @modelcontextprotocol/sdk, zod — nothing else

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Environment Variables

Variable	Required	Default	Description
MESHY_API_KEY	✅	—	From app.meshy.ai → Settings → API Keys
MESHY_BASE_URL	—	https://api.meshy.ai	Override API base URL
MESHY_POLL_TIMEOUT_SECONDS	—	300	Max wait per task
MESHY_POLL_INTERVAL_SECONDS	—	3	Polling frequency

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Contributing

1. Fork → create a branch → npm run typecheck before committing
2. New tool? Follow the pattern in any existing file in src/tools/
3. Open a PR — see CLAUDE.md for the full contributor guide

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Related

• Meshy AI — the 3D generation API powering this server
• Model Context Protocol — the open protocol for Claude tool use
• ThreadForge — clothing brand mockup platform (pairs well with meshy-mcp)
• Claude Desktop — the MCP client used in examples
• How Meshy Built a $20M+ 3D AI Business — deep dive into the data flywheel, training strategy, and business model

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License

MIT

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