Which integrations are available for this server?

Enables remote execution of [Python](/mcp/servers/integrations/python) code in Rhino/Grasshopper, allowing AI to automate computational design workflows, generate scripts, and interact with the Grasshopper canvas in real-time. Powers ML-based layout optimization features including DBSCAN and K-means clustering for component grouping, K-NN position prediction for component placement, and pattern learning for Grasshopper definitions.

🚀 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.

Version Status License: MIT Python 3.10+

🎯 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

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:

Open Rhino 7 or 8
Run EditPythonScript command
Open and run rhino_listener/rhino_bridge_listener.py
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

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

Feature Extraction: Analyzes component positions, types, and connections
Clustering: Groups components using DBSCAN or K-means
Pattern Learning: Stores user preferences in layout_preferences.json
Position Prediction: Uses K-NN to suggest optimal positions for new components
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

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

Need Help?

Report Issue

Related Servers

Grasshopper MCP