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

1. Clone the repository:

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

2. Create a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Install OpenSCAD:

5. 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

6. Set up 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)

1. 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

2. Start the remote CUDA MVS server:

python src/main_remote.py

3. The server will automatically advertise itself on the local network using Zeroconf.

Client Configuration

1. 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

2. 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

1. Start the server:

python src/main.py

2. The server will start on http://localhost:8000

3. Use the MCP tools to interact with the server:

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

{
       "prompt": "A low-poly rabbit",
       "num_views": 4
     }

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

{
       "prompt": "A low-poly rabbit",
       "num_views": 4
     }

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

{
       "timeout": 5
     }

{
       "server_id": "server-id",
       "job_id": "job-id"
     }

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

{}

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

Image Generation Options

The server supports multiple image generation options:

1. Google Gemini API (Default): Uses the Gemini 2.0 Flash Experimental model for high-quality image generation

2. Venice.ai API (Optional): Alternative image generation service

3. User-Provided Images: Skip image generation and use your own images

Multi-View Workflow

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

1. Image Generation: Generate multiple views of the same 3D object

2. Image Approval: Review and approve/deny each generated image

3. 3D Reconstruction: Convert approved images into a 3D model using CUDA MVS

4. 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:

1. Server Discovery: Automatically discover CUDA MVS servers on your network

2. Image Upload: Upload approved multi-view images to the remote server

3. Job Processing: Process the images on the remote server using CUDA MVS

4. Status Tracking: Monitor the job status and progress

5. Result Download: Download the completed 3D model when processing is finished

Supported Export Formats

The server supports exporting models in various formats:

• OBJ: Wavefront OBJ format (standard 3D model format)
• STL: Standard Triangle Language (for 3D printing)
• PLY: Polygon File Format (for point clouds and meshes)
• SCAD: OpenSCAD source code (for parametric models)
• CSG: OpenSCAD CSG format (preserves all parametric properties)
• AMF: Additive Manufacturing File Format (preserves some metadata)
• 3MF: 3D Manufacturing Format (modern replacement for STL with metadata)

Web Interface

The server provides a web interface for:

• Generating and approving multi-view images
• Previewing 3D models from different angles
• Downloading models in various formats

Access the interface at http://localhost:8000/ui/

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.