# SMILES Visualizer MCP Server - Project Summary
## Overview
The SMILES Visualizer MCP Server is a Model Context Protocol (MCP) server that provides molecular visualization capabilities for SMILES (Simplified Molecular Input Line Entry System) strings. It offers multiple visualization approaches including RDKit, NetworkX, Plotly, and custom matplotlib visualizations.
## Key Features
### 🔬 Molecular Processing
- **SMILES Validation**: Built-in validation using RDKit
- **Molecular Information**: Detailed molecular properties and descriptors
- **Multiple Formats**: Support for various molecular representations
### 🎨 Visualization Types
1. **RDKit 2D Structures**: Standard molecular structure diagrams
2. **Network Graphs**: Graph-based molecular representations
3. **Interactive Plotly**: Rich interactive visualizations
4. **Custom Matplotlib**: Detailed analytical plots
### 🚀 MCP Integration
- **HTTP Streamable Transport**: Modern MCP transport protocol
- **Tool-based Architecture**: Clean, modular tool design
- **Client Compatibility**: Works with any MCP-compatible client
### 📊 Output Formats
- **Base64 Images**: PNG format for easy embedding
- **Interactive HTML**: Plotly visualizations
- **Structured JSON**: Molecular data and properties
## Project Structure
```
smiles_visualizer_mcp/
├── server.py # Main MCP server implementation
├── requirements.txt # Python dependencies
├── README.md # Comprehensive documentation
├── QUICKSTART.md # Quick start guide
├── test_server.py # Test suite
├── example.py # Usage examples
├── setup.py # Package setup
├── Dockerfile # Docker configuration
├── docker-compose.yml # Docker Compose setup
├── .gitignore # Git ignore rules
└── PROJECT_SUMMARY.md # This file
```
## Available Tools
### Core Tools
1. **`validate_smiles`** - Validate SMILES strings using RDKit
2. **`get_molecular_info`** - Get detailed molecular information and properties
3. **`visualize_rdkit`** - Create RDKit 2D molecular visualizations
4. **`visualize_network`** - Create network graph visualizations
5. **`visualize_plotly`** - Create interactive Plotly visualizations
6. **`visualize_custom_matplotlib`** - Create custom matplotlib visualizations
7. **`compare_visualizations`** - Generate all visualization types for comparison
8. **`batch_visualize`** - Process multiple SMILES strings
### Molecular Properties Calculated
- Molecular weight
- Number of atoms, bonds, and rings
- Molecular formula
- LogP and molar refractivity
- Topological polar surface area (TPSA)
- Number of rotatable bonds
- Hydrogen bond donors/acceptors
## Technical Implementation
### Architecture
- **FastMCP Framework**: Uses the official MCP Python SDK
- **Async/Await**: Full asynchronous support for high performance
- **Error Handling**: Comprehensive error handling and validation
- **Modular Design**: Clean separation of concerns
### Dependencies
- **Core**: `mcp[cli]`, `rdkit-pypi`, `matplotlib`, `networkx`, `plotly`
- **Support**: `numpy`, `pandas`, `pillow`, `uvicorn`, `fastapi`
- **Optional**: `seaborn` for enhanced plotting
### Transport
- **HTTP Streamable**: Primary transport mechanism
- **Configurable**: Host and port configuration
- **Production Ready**: Docker support and health checks
## Usage Examples
### Basic Usage
```bash
# Start the server
python server.py --host 127.0.0.1 --port 8001
# Test with curl
curl -X POST http://127.0.0.1:8001/tools/validate_smiles/call \
-H "Content-Type: application/json" \
-d '{"arguments": {"smiles": "CCO"}}'
```
### Docker Deployment
```bash
# Using Docker Compose
docker-compose up -d
# Manual Docker build
docker build -t smiles-visualizer-mcp .
docker run -p 8001:8001 smiles-visualizer-mcp
```
### MCP Client Integration
```json
{
"mcpServers": {
"smiles-visualizer": {
"command": "python",
"args": ["/path/to/smiles_visualizer_mcp/server.py"],
"env": {
"MCP_HOST": "127.0.0.1",
"MCP_PORT": "8001"
}
}
}
}
```
## Example Molecules
The server works with various types of molecules:
- **Simple molecules**: `CCO` (ethanol)
- **Drug molecules**: `CC(C)CC1=CC=C(C=C1)C(C)C(=O)O` (ibuprofen)
- **Complex structures**: `CN1C=NC2=C1C(=O)N(C(=O)N2C)C` (caffeine)
- **Aromatic compounds**: `C1=CC=C(C=C1)C2=CC=CC=C2` (biphenyl)
## Development Features
### Testing
- **Comprehensive Test Suite**: Full coverage of all tools
- **Automated Testing**: Easy to run test scripts
- **Error Simulation**: Tests for various error conditions
### Documentation
- **Detailed README**: Complete usage documentation
- **Quick Start Guide**: Fast setup instructions
- **Code Examples**: Practical usage examples
- **API Documentation**: Tool specifications
### Deployment
- **Docker Support**: Containerized deployment
- **Environment Variables**: Flexible configuration
- **Health Checks**: Production-ready monitoring
- **Logging**: Comprehensive logging system
## Benefits
### For Researchers
- **Multiple Visualization Types**: Choose the best representation for your needs
- **Batch Processing**: Handle multiple molecules efficiently
- **Rich Molecular Data**: Access to comprehensive molecular properties
### For Developers
- **MCP Standard**: Follows established protocol standards
- **Easy Integration**: Simple to integrate with existing systems
- **Extensible**: Easy to add new visualization types
### For Users
- **Interactive Visualizations**: Rich, interactive molecular representations
- **High-Quality Output**: Professional-grade visualizations
- **Fast Processing**: Efficient handling of molecular data
## Future Enhancements
### Potential Additions
- **3D Visualizations**: Add 3D molecular representations
- **Molecular Dynamics**: Support for trajectory visualization
- **Property Prediction**: Machine learning-based property prediction
- **Database Integration**: Connect to molecular databases
- **Export Formats**: Support for additional output formats
### Performance Improvements
- **Caching**: Implement result caching for better performance
- **Parallel Processing**: Support for concurrent molecule processing
- **Optimization**: Further optimize visualization generation
## Conclusion
The SMILES Visualizer MCP Server provides a comprehensive solution for molecular visualization through the Model Context Protocol. It offers multiple visualization approaches, detailed molecular information, and easy integration with MCP-compatible clients. The server is production-ready with Docker support, comprehensive testing, and detailed documentation.
This project successfully demonstrates how to create a specialized MCP server for scientific applications, providing valuable tools for researchers and developers working with molecular data.
