Meshy Ai MCP Server

🚀 Meshy AI MCP Server

This is a Model Context Protocol (MCP) server designed to interact with the Meshy AI API. It offers functions for generating 3D models, applying textures, and optimizing models.

🚀 Quick Start

This Meshy AI MCP Server enables seamless interaction with the Meshy AI API, providing a range of features for 3D model generation and manipulation.

✨ 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
• View 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
   

4. Install dependencies:

   pip install -r requirements.txt
   

5. Create a .env file and add your Meshy AI API key:

   cp .env.example .env
   # Edit .env and add your API key
   

💻 Usage Examples

Starting the Server

You can start the server using the following command:

python -m mcp.server --port 8081

By default, the server will run on port 8081. You can specify a different port in the MCP_PORT environment variable.

Examples

Basic Usage

Generate a 3D model:

from mcp.client import MCPClient

client = MCPClient()
result = client.use_tool(
    "meshy-ai",
    "create_text_to_3d_model",
    {
        "request": {
            "mode": "preview",
            "prompt": "a monster mask",
            "art_style": "realistic",
            "should_remesh": True
        }
    }
)
print(f"Task ID: {result['id']}")

Advanced Usage

Check the task status:

from mcp.client import MCPClient

client = MCPClient()
task_id = "your-task-id"
result = client.use_tool(
    "meshy-ai",
    "retrieve_text_to_3d_model_task",
    {
        "task_id": task_id
    }
)
print(f"Status: {result['status']}")

📚 Documentation

You can configure the server through environment variables:

📄 License

This project is licensed under the MIT License. Please refer to the LICENSE file for detailed information.