meshy-ai-mcp-server
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Meshy AI MCP Server is a Model Context Protocol server designed to interact with the Meshy AI API, enabling the generation and manipulation of 3D models from text and images.
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/activateUsing 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
.envfile 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 promptcreate_image_to_3d_task: Generate a 3D model from an imagecreate_text_to_texture_task: Apply textures to a 3D model using text promptscreate_remesh_task: Remesh and optimize a 3D model
Retrieval Tools
retrieve_text_to_3d_task: Get details of a Text to 3D taskretrieve_image_to_3d_task: Get details of an Image to 3D taskretrieve_text_to_texture_task: Get details of a Text to Texture taskretrieve_remesh_task: Get details of a Remesh task
Listing Tools
list_text_to_3d_tasks: List Text to 3D taskslist_image_to_3d_tasks: List Image to 3D taskslist_text_to_texture_tasks: List Text to Texture taskslist_remesh_tasks: List Remesh tasks
Streaming Tools
stream_text_to_3d_task: Stream updates for a Text to 3D taskstream_image_to_3d_task: Stream updates for an Image to 3D taskstream_text_to_texture_task: Stream updates for a Text to Texture taskstream_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 endpointtask://{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.
