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

Clone this repository:

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

(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

Install the MCP package:
```
pip install mcp
```
Install dependencies:
```
pip install -r requirements.txt
```

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

You can open the inspector URL in your browser to monitor MCP communication.

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.