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.

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

Installation

git clone https://github.com/dongwoosuk/grasshopper-mcp.git
cd grasshopper-mcp
python -m venv .venv
.venv\Scripts\activate  # Windows
pip install -e .

Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "grasshopper": {
      "command": "path/to/grasshopper_mcp/.venv/Scripts/python.exe",
      "args": ["-m", "grasshopper_mcp"],
      "cwd": "path/to/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)

AI Mentoring Tools

Pattern Detection Examples

The suggest_alternatives tool detects these inefficient patterns:

Architecture

grasshopper_mcp/
 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

# 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 file.

Acknowledgments

• Built on the Model Context Protocol by Anthropic
• Grasshopper by David Rutten / McNeel
• scikit-learn for ML algorithms

Contact

Dongwoo Suk - Computational Design Specialist

• GitHub: dongwoosuk
• LinkedIn: dongwoosuk