Grasshopper MCP

# 🚀 Rhino Grasshopper MCP - AI-Powered Computational Design Assistant An MCP (Model Context Protocol) server that brings AI capabilities directly into Rhino/Grasshopper workflows, featuring **ML-based automatic layout optimization** - a capability not found in any other publicly available tool.   [](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) ## 🎯 What Makes This Different? Unlike basic Grasshopper-to-LLM connectors that only generate code, this tool includes: ### 🧠 ML-Based Auto Layout (Industry First) - **DBSCAN clustering** for automatic component grouping - **K-means clustering** for fixed group counts - **User pattern learning** with persistent preferences - **K-NN position prediction** for optimal new component placement - **Wire crossing minimization** algorithm ### 🎓 AI Mentoring System - **Performance prediction**: Estimates optimization impact (e.g., "15-30% improvement expected") - **Pattern detection**: Identifies inefficient patterns and suggests better alternatives - **Auto-grouping**: Analyzes wire connectivity to suggest logical component groups - **Layout anomaly detection**: Finds isolated, overlapping, or misaligned components ### 🔌 Live Rhino Integration - Real-time connection to running Rhino/Grasshopper instances - Execute Python code remotely in Rhino - Get canvas state and trigger solves - No file-based workflow required ## 📋 Features Overview | Category | Features | |----------|----------| | **Rhino Bridge** | Live connection, remote Python execution, canvas state | | **GH File Ops** | Parse .gh/.ghx files, analyze structure, extract components | | **Component Library** | Searchable database of 500+ GH components | | **Code Generation** | GHPython and C# script templates | | **AI Mentoring** | Performance prediction, alternatives, auto-grouping | | **ML Layout** | Clustering, position prediction, crossing minimization | ## 🛠️ Installation ```bash git clone https://github.com/dongwoosuk/rhino-grasshopper-mcp.git cd rhino-grasshopper-mcp python -m venv .venv .venv\Scripts\activate # Windows pip install -e . ``` ## ⚙️ Configuration Add to your `claude_desktop_config.json`: ```json { "mcpServers": { "grasshopper": { "command": "path/to/rhino_grasshopper_mcp/.venv/Scripts/python.exe", "args": ["-m", "rhino_grasshopper_mcp"] } } } ``` ## 🔌 Live Rhino Connection (Optional) To enable real-time Rhino/Grasshopper control: 1. Open Rhino 7 or 8 2. Run `EditPythonScript` command 3. Open and run `rhino_listener/rhino_bridge_listener.py` 4. You'll see: `Rhino Bridge Listener started on localhost:8080` ## 📚 Available Tools ### Rhino Bridge (requires listener) | Tool | Description | |------|-------------| | `rhino_status` | Check connection status | | `rhino_execute_python` | Execute Python in Rhino | | `gh_canvas_state` | Get current canvas state | | `gh_load_definition` | Load a .gh file | | `gh_solve` | Trigger recompute | ### AI Mentoring Tools | Tool | Description | |------|-------------| | `predict_performance` | Predict optimization impact | | `suggest_alternatives` | Detect patterns, suggest better approaches | | `auto_group` | Analyze connectivity, suggest groupings | | `ml_layout_analysis` | ML clustering analysis | | `predict_component_position` | K-NN based position prediction | ### Pattern Detection Examples The `suggest_alternatives` tool detects these inefficient patterns: | Pattern | Better Alternative | |---------|-------------------| | Multiple Move components | Single Transform Matrix | | Flatten + Graft sequence | Path Mapper | | Python loops for geometry | Native GH components | | Serial Boolean operations | Batch Boolean | | Expression for simple math | Native Math components | ## 🏗️ Architecture ``` rhino_grasshopper_mcp/ ├── rhino_grasshopper_mcp/ │ ├── bridge.py # Main MCP server │ ├── rhino_bridge.py # TCP client to Rhino │ ├── gh_file_ops.py # .gh/.ghx parsing │ ├── component_library.py # Component database │ ├── code_generator.py # Code templates │ └── mentoring/ │ ├── ml_layout_learner.py # DBSCAN/K-means │ ├── advanced_layout_learner.py # Advanced learning │ ├── persistent_layout_learner.py # User patterns │ ├── performance_predictor.py # Optimization prediction │ ├── alternative_suggester.py # Pattern detection │ ├── auto_grouper.py # Wire-based grouping │ ├── wire_crossing_detector.py # Crossing detection │ └── crossing_minimizer.py # Layout optimization ├── rhino_listener/ │ └── rhino_bridge_listener.py # Run IN Rhino └── pyproject.toml ``` ## 🤖 ML Layout System Details ### How It Works 1. **Feature Extraction**: Analyzes component positions, types, and connections 2. **Clustering**: Groups components using DBSCAN or K-means 3. **Pattern Learning**: Stores user preferences in `layout_preferences.json` 4. **Position Prediction**: Uses K-NN to suggest optimal positions for new components 5. **Anomaly Detection**: Identifies layout issues (isolated nodes, overlaps, wrong flow direction) ### Supported Analyses ```python # Example: ML clustering analysis result = ml_layout_analysis(gh_file_path, method="dbscan") # Returns: clusters, anomalies, optimization suggestions # Example: Position prediction result = predict_component_position(gh_file_path, "Panel", near_component="Slider") # Returns: predicted X, Y coordinates based on learned patterns ``` ## 🎯 Use Cases - **Design Automation**: Let AI handle repetitive Grasshopper tasks - **Code Review**: Get suggestions for optimizing definitions - **Learning**: AI mentoring for Grasshopper best practices - **Layout Cleanup**: Automatic organization of messy definitions - **Team Standards**: Consistent component arrangement across projects ## 📄 License MIT License - see [LICENSE](LICENSE) file. ## 🙏 Acknowledgments - Built on the [Model Context Protocol](https://github.com/anthropics/anthropic-cookbook/tree/main/misc/model_context_protocol) by Anthropic - Grasshopper by David Rutten / McNeel - scikit-learn for ML algorithms ## 📬 Contact Dongwoo Suk - Computational Design Specialist - GitHub: [dongwoosuk](https://github.com/dongwoosuk) - LinkedIn: [dongwoosuk](https://www.linkedin.com/in/dongwoosuk/)