OpenSCAD MCP Server

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MCP-Mirror9 months ago

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OpenSCAD MCP Server

A Model Context Protocol (MCP) server that enables users to generate 3D models from text descriptions or images, with a focus on creating parametric 3D models using multi-view reconstruction and OpenSCAD.

Features

AI Image Generation: Generate images from text descriptions using Google Gemini or Venice.ai APIs
Multi-View Image Generation: Create multiple views of the same 3D object for reconstruction
Image Approval Workflow: Review and approve/deny generated images before reconstruction
3D Reconstruction: Convert approved multi-view images into 3D models using CUDA Multi-View Stereo
Remote Processing: Process computationally intensive tasks on remote servers within your LAN
OpenSCAD Integration: Generate parametric 3D models using OpenSCAD
Parametric Export: Export models in formats that preserve parametric properties (CSG, AMF, 3MF, SCAD)
3D Printer Discovery: Optional network printer discovery and direct printing

Architecture

The server is built using the Python MCP SDK and follows a modular architecture:

openscad-mcp-server/
├── src/
│   ├── main.py                  # Main application
│   ├── main_remote.py           # Remote CUDA MVS server
│   ├── ai/                      # AI integrations
│   │   ├── gemini_api.py        # Google Gemini API for image generation
│   │   └── venice_api.py        # Venice.ai API for image generation (optional)
│   ├── models/                  # 3D model generation
│   │   ├── cuda_mvs.py          # CUDA Multi-View Stereo integration
│   │   └── code_generator.py    # OpenSCAD code generation
│   ├── workflow/                # Workflow components
│   │   ├── image_approval.py    # Image approval mechanism
│   │   └── multi_view_to_model_pipeline.py  # Complete pipeline
│   ├── remote/                  # Remote processing
│   │   ├── cuda_mvs_client.py   # Client for remote CUDA MVS processing
│   │   ├── cuda_mvs_server.py   # Server for remote CUDA MVS processing
│   │   ├── connection_manager.py # Remote connection management
│   │   └── error_handling.py    # Error handling for remote processing
│   ├── openscad_wrapper/        # OpenSCAD CLI wrapper
│   ├── visualization/           # Preview generation and web interface
│   ├── utils/                   # Utility functions
│   └── printer_discovery/       # 3D printer discovery
├── scad/                        # Generated OpenSCAD files
├── output/                      # Output files (models, previews)
│   ├── images/                  # Generated images
│   ├── multi_view/              # Multi-view images
│   ├── approved_images/         # Approved images for reconstruction
│   └── models/                  # Generated 3D models
├── templates/                   # Web interface templates
└── static/                      # Static files for web interface

Installation

Clone the repository:

git clone https://github.com/jhacksman/OpenSCAD-MCP-Server.git
cd OpenSCAD-MCP-Server

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:
```
pip install -r requirements.txt
```
Install OpenSCAD:
- Ubuntu/Debian: sudo apt-get install openscad
- macOS: brew install openscad
- Windows: Download from openscad.org

Install CUDA Multi-View Stereo:

git clone https://github.com/fixstars/cuda-multi-view-stereo.git
cd cuda-multi-view-stereo
mkdir build && cd build
cmake ..
make

Set up API keys:

Create a .env file in the root directory

Add your API keys:

GEMINI_API_KEY=your-gemini-api-key
VENICE_API_KEY=your-venice-api-key  # Optional
REMOTE_CUDA_MVS_API_KEY=your-remote-api-key  # For remote processing

Remote Processing Setup

The server supports remote processing of computationally intensive tasks, particularly CUDA Multi-View Stereo reconstruction. This allows you to offload processing to more powerful machines within your LAN.

Server Setup (on the machine with CUDA GPU)

Install CUDA Multi-View Stereo on the server machine:

git clone https://github.com/fixstars/cuda-multi-view-stereo.git
cd cuda-multi-view-stereo
mkdir build && cd build
cmake ..
make

Start the remote CUDA MVS server:
```
python src/main_remote.py
```
The server will automatically advertise itself on the local network using Zeroconf.

Client Configuration

Configure remote processing in your .env file:

REMOTE_CUDA_MVS_ENABLED=True
REMOTE_CUDA_MVS_USE_LAN_DISCOVERY=True
REMOTE_CUDA_MVS_API_KEY=your-shared-secret-key

Alternatively, you can specify a server URL directly:

REMOTE_CUDA_MVS_ENABLED=True
REMOTE_CUDA_MVS_USE_LAN_DISCOVERY=False
REMOTE_CUDA_MVS_SERVER_URL=http://server-ip:8765
REMOTE_CUDA_MVS_API_KEY=your-shared-secret-key

Remote Processing Features

Automatic Server Discovery: Find CUDA MVS servers on your local network
Job Management: Upload images, track job status, and download results
Fault Tolerance: Automatic retries, circuit breaker pattern, and error tracking
Authentication: Secure API key authentication for all remote operations
Health Monitoring: Continuous server health checks and status reporting

Usage

Start the server:
```
python src/main.py
```
The server will start on http://localhost:8000

Use the MCP tools to interact with the server:

generate_image_gemini: Generate an image using Google Gemini API

{
  "prompt": "A low-poly rabbit with black background",
  "model": "gemini-2.0-flash-exp-image-generation"
}

generate_multi_view_images: Generate multiple views of the same 3D object
```
{
  "prompt": "A low-poly rabbit",
  "num_views": 4
}
```

create_3d_model_from_images: Create a 3D model from approved multi-view images

{
  "image_ids": ["view_1", "view_2", "view_3", "view_4"],
  "output_name": "rabbit_model"
}

create_3d_model_from_text: Complete pipeline from text to 3D model
```
{
  "prompt": "A low-poly rabbit",
  "num_views": 4
}
```

export_model: Export a model to a specific format

{
  "model_id": "your-model-id",
  "format": "obj"  // or "stl", "ply", "scad", etc.
}

discover_remote_cuda_mvs_servers: Find CUDA MVS servers on your network
```
{
  "timeout": 5
}
```
get_remote_job_status: Check the status of a remote processing job
```
{
  "server_id": "server-id",
  "job_id": "job-id"
}
```

download_remote_model_result: Download a completed model from a remote server

{
  "server_id": "server-id",
  "job_id": "job-id",
  "output_name": "model-name"
}

discover_printers: Discover 3D printers on the network
```
{}
```

print_model: Print a model on a connected printer

{
  "model_id": "your-model-id",
  "printer_id": "your-printer-id"
}

Image Generation Options

The server supports multiple image generation options:

Google Gemini API (Default): Uses the Gemini 2.0 Flash Experimental model for high-quality image generation
- Supports multi-view generation with consistent style
- Requires a Google Gemini API key
Venice.ai API (Optional): Alternative image generation service
- Supports various models including flux-dev and fluently-xl
- Requires a Venice.ai API key
User-Provided Images: Skip image generation and use your own images
- Upload images directly to the server
- Useful for working with existing photographs or renders

Multi-View Workflow

The server implements a multi-view workflow for 3D reconstruction:

Image Generation: Generate multiple views of the same 3D object
Image Approval: Review and approve/deny each generated image
3D Reconstruction: Convert approved images into a 3D model using CUDA MVS
- Can be processed locally or on a remote server within your LAN
Model Refinement: Optionally refine the model using OpenSCAD

Remote Processing Workflow

The remote processing workflow allows you to offload computationally intensive tasks to more powerful machines:

Server Discovery: Automatically discover CUDA MVS servers on your network
Image Upload: Upload approved multi-view images to the remote server
Job Processing: Process the images on the remote server using CUDA MVS
Status Tracking: Monitor the job status and progress
Result Download: Download the completed 3D model when processing is finished