Meshy AI MCP Server

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.

Features

- Generate 3D models from text prompts
- Generate 3D models from images
- Apply textures to 3D models
- Remesh and optimize 3D models
- Stream task progress in real-time
- List and retrieve tasks
- Check account balance

Installation

1. Clone this repository:

   git clone https://github.com/pasie15/scenario.com-mcp-server

   cd meshy-ai-mcp-server

   

2. (Recommended) Set up a virtual environment:

Using venv:

   python -m venv .venv

   # On Windows

   .\.venv\Scripts\activate

   # On macOS/Linux

   source .venv/bin/activate

   

Using Conda:

   conda create --name meshy-mcp python=3.9  # Or your preferred Python version

   conda activate meshy-mcp

   

3. Install the MCP package:

   pip install mcp

   

   pip install -r requirements.txt

   

6. Create a .env file with your Meshy AI API key:

   cp .env.example .env

   # Edit .env and add your API key

   

Usage

Starting the Server

You can start the server directly with Python:

python src/server.py

Or using the MCP CLI:

mcp run config.json

Editor Configuration

Add this MCP server configuration to your Cline/Roo-Cline/Cursor/VS Code settings (e.g., .vscode/settings.json or user settings):

{
  "mcpServers": {
    "meshy-ai": {
      "command": "python",
      "args": [
        "path/to/your/meshy-ai-mcp-server/src/server.py"  // <-- Make sure this path is correct!
      ],
      "disabled": false,
      "autoApprove": [],
      "alwaysAllow": []
    }
  }
}

Recommended: Using MCP dev mode (starts inspector)

For development and debugging, run the server using mcp dev:

mcp dev src/server.py

When running with mcp dev, you'll see output like:

Starting MCP inspector...
⚙️ Proxy server listening on port 6277
🔍 MCP Inspector is up and running at http://127.0.0.1:6274 🚀
New SSE connection

Available Tools

The server provides the following tools:

Creation Tools

- create_text_to_3d_task: Generate a 3D model from a text prompt
- create_image_to_3d_task: Generate a 3D model from an image
- create_text_to_texture_task: Apply textures to a 3D model using text prompts
- create_remesh_task: Remesh and optimize a 3D model

Retrieval Tools

- retrieve_text_to_3d_task: Get details of a Text to 3D task
- retrieve_image_to_3d_task: Get details of an Image to 3D task
- retrieve_text_to_texture_task: Get details of a Text to Texture task
- retrieve_remesh_task: Get details of a Remesh task

Listing Tools

- list_text_to_3d_tasks: List Text to 3D tasks
- list_image_to_3d_tasks: List Image to 3D tasks
- list_text_to_texture_tasks: List Text to Texture tasks
- list_remesh_tasks: List Remesh tasks

Streaming Tools

- stream_text_to_3d_task: Stream updates for a Text to 3D task
- stream_image_to_3d_task: Stream updates for an Image to 3D task
- stream_text_to_texture_task: Stream updates for a Text to Texture task
- stream_remesh_task: Stream updates for a Remesh task

Utility Tools

- get_balance: Check your Meshy AI account balance

Resources

The server also provides the following resources:

- health://status: Health check endpoint
- task://{task_type}/{task_id}: Access task details by type and ID

Configuration

The server can be configured using environment variables:

- MESHY_API_KEY: Your Meshy AI API key (required)
- MCP_PORT: Port for the MCP server to listen on (default: 8081)
- TASK_TIMEOUT: Maximum time to wait for a task to complete when streaming (default: 300 seconds)

Examples

Generating a 3D Model from Text

from mcp.client import MCPClient
client = MCPClient()

result = client.use_tool(

    "meshy-ai",

    "create_text_to_3d_task",

    {

        "request": {

            "mode": "preview",

            "prompt": "a monster mask",

            "art_style": "realistic",

            "should_remesh": True

        }

    }

)

print(f"Task ID: {result['id']}")

Checking Task Status

from mcp.client import MCPClient
client = MCPClient()

task_id = "your-task-id"

result = client.use_tool(

    "meshy-ai",

    "retrieve_text_to_3d_task",

    {

        "task_id": task_id

    }

)

print(f"Status: {result['status']}")

License

This project is licensed under the MIT License - see the LICENSE file for details.