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Meshy Ai

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This is a Model Context Protocol (MCP) server for interacting with the Meshy AI API. It provides tools for generating 3D models from text and images, applying t

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Описание

This is a Model Context Protocol (MCP) server for interacting with the Meshy AI API. It provides tools for generating 3D models from text and images, applying textures, and remeshing models.

README

This is a Model Context Protocol (MCP) server that wraps the Meshy AI API. It enables MCP clients (like Claude Desktop, Cursor, Cline) to interact with Meshy's generative 3D tools directly.

Features

  • Text-to-3D: Generate 3D models from text prompts.
  • Image-to-3D: Create 3D models from reference images.
  • Multi-Image-to-3D: Create 3D models from multiple reference images.
  • Retexture: Apply new textures to existing 3D models. The text-to-texture tools are compatibility aliases for the same retexture API.
  • Text-to-Image: Generate images from text prompts.
  • Image-to-Image: Generate new images from input images.
  • Model Optimization: Remesh and optimize geometry.
  • Rigging: Auto-rig 3D characters for animation.
  • Animation: Apply animations to rigged characters.
  • Streaming: Real-time progress updates for long-running tasks.
  • Task Deletion: Delete tasks across all API categories.

Installation

Option 1: Run directly with npx (Recommended)

You can run the server directly using npx without installing it globally.

{
  "mcpServers": {
    "meshy-ai": {
      "command": "npx",
      "args": [
        "-y",
        "meshy-ai-mcp-server"
      ],
      "env": {
        "MESHY_API_KEY": "your_meshy_api_key_here"
      }
    }
  }
}

Option 2: Clone and Build Locally

If you want to modify the code or run it from a local source:

  1. Clone the repository:

    git clone <repository-url>
    cd meshy-ai-mcp-server
    
  2. Install dependencies:

    npm install
    
  3. Build the project:

    npm run build
    
  4. Configure your MCP Client:

    Add the following to your MCP client configuration (e.g., claude_desktop_config.json or VS Code settings):

    {
      "mcpServers": {
        "meshy-ai": {
          "command": "node",
          "args": [
            "/absolute/path/to/meshy-ai-mcp-server/dist/index.js"
          ],
          "env": {
            "MESHY_API_KEY": "your_meshy_api_key_here"
          }
        }
      }
    }
    

Configuration

You need a Meshy AI API key to use this server.

  1. Get your API key from the Meshy Dashboard.
  2. Set the MESHY_API_KEY environment variable in your MCP client configuration (as shown above).

Optional Environment Variables

  • MESHY_API_BASE: Override the API base URL (default: https://api.meshy.ai/openapi).
  • MESHY_STREAM_TIMEOUT_MS: Timeout for streaming responses in milliseconds (default: 300000 aka 5 minutes).

Troubleshooting

If your MCP client reports that the server closed during initialize, check that the client configuration passes MESHY_API_KEY into the server process. The server can start without the key so clients can inspect available tools, but Meshy API tool calls will fail until the key is configured.

Development

To run the server in development mode with auto-reloading:

# Create a .env file
echo "MESHY_API_KEY=your_key_here" > .env

# Run in dev mode
npm run dev

Available Tools

  • Text to 3D: create_text_to_3d_task, retrieve_text_to_3d_task, list_text_to_3d_tasks, stream_text_to_3d_task, delete_text_to_3d_task
  • Image to 3D: create_image_to_3d_task, retrieve_image_to_3d_task, list_image_to_3d_tasks, stream_image_to_3d_task, delete_image_to_3d_task
  • Multi-Image to 3D: create_multi_image_to_3d_task, retrieve_multi_image_to_3d_task, list_multi_image_to_3d_tasks, stream_multi_image_to_3d_task, delete_multi_image_to_3d_task
  • Retexture: create_retexture_task, retrieve_retexture_task, list_retexture_tasks, stream_retexture_task, delete_retexture_task
  • Texturing (retexture aliases): create_text_to_texture_task, retrieve_text_to_texture_task, list_text_to_texture_tasks, stream_text_to_texture_task, delete_text_to_texture_task — same retexture API, historical tool names kept for compatibility.
  • Text to Image: create_text_to_image_task, retrieve_text_to_image_task, list_text_to_image_tasks, stream_text_to_image_task, delete_text_to_image_task
  • Image to Image: create_image_to_image_task, retrieve_image_to_image_task, list_image_to_image_tasks, stream_image_to_image_task, delete_image_to_image_task
  • Remeshing: create_remesh_task, retrieve_remesh_task, list_remesh_tasks, stream_remesh_task, delete_remesh_task
  • Rigging: create_rigging_task, retrieve_rigging_task, list_rigging_tasks, stream_rigging_task, delete_rigging_task
  • Animation: create_animation_task, retrieve_animation_task, list_animation_tasks, stream_animation_task, delete_animation_task
  • Utility: get_balance

License

MIT

from github.com/pasie15/meshy-ai-mcp-server

Установка Meshy Ai

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/pasie15/meshy-ai-mcp-server

FAQ

Meshy Ai MCP бесплатный?

Да, Meshy Ai MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Meshy Ai?

Да, требуются переменные окружения: MESHY_API_KEY. Unyly подставит их в конфиг при установке.

Meshy Ai — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Meshy Ai в Claude Desktop, Claude Code или Cursor?

Открой Meshy Ai на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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