DROMA MCP Server
About
A Model Context Protocol (MCP) server for DROMA (Drug Response Omics association MAp) - enabling natural language interactions with drug-omics association analysis.
Details
- Author
- mugpeng
- Downloads
- 240
- Categories
- Other, Database
Jump to
- Natural language interface for drug-omics association queries
- Dataset management (CCLE, gCSI, etc.) in memory
- Data loading with automatic z-score normalization
- Multi-project support for cross-study analysis
- Smart caching with metadata tracking and LRU eviction
- Multi-modal transport (STDIO, HTTP, SSE)
- R integration via rpy2 for seamless package use
- Performance optimizations (async, connection pooling, memory management)
Install via pip install droma-mcp. Requires Python 3.10+, R 4.0+ with DROMA R packages, and a DROMA SQLite database. Start the server with droma-mcp run --db-path path/to/droma.sqlite. Use CLI commands like validate, test, benchmark, and export-config to configure and verify the environment.
DROMA MCP Server
A Model Context Protocol (MCP) server for DROMA (Drug Response Omics association MAp) - enabling natural language interactions with drug-omics association analysis.
🚀 Overview
DROMA MCP Server bridges the gap between AI assistants and cancer pharmacogenomics analysis by providing a natural language interface to the DROMA.R and DROMA.Set packages.
Key Features
- 🔗 Natural Language Interface: Ask questions about drug-omics associations in plain English
- 📊 Dataset Management: Load and manage DROMA datasets (CCLE, gCSI, etc.) in memory
- 📈 Data Loading & Normalization: Load molecular profiles and treatment response data with automatic z-score normalization
- 🗂️ Multi-Project Support: Seamlessly work with data across multiple research projects
- 💾 Smart Caching: Efficient data caching with metadata tracking for faster access
- 📤 Data Export: Export analysis results to various formats (CSV, Excel, JSON)
- ⚡ Multi-Modal Support: Works with various transport protocols (STDIO, HTTP, SSE)
- 🔄 R Integration: Seamless integration with existing DROMA R packages via rpy2
- 🚄 Performance Optimizations: Memory management, asynchronous processing, and connection pooling
- 🛡️ Robust Error Handling: Comprehensive validation, logging, and graceful error recovery
- 🎛️ Class-Based CLI: Modern, type-safe command-line interface with comprehensive help
🏎️ Performance Features
- Asynchronous Processing: Non-blocking I/O operations for better responsiveness
- Memory Management: Automatic memory monitoring and garbage collection
- Connection Pooling: Efficient R environment management
- Smart Caching: LRU cache with size limits and automatic eviction
- Batch Operations: Process multiple datasets efficiently
- Performance Monitoring: Built-in metrics tracking and reporting
📦 Installation
Prerequisites
- Python 3.10+
- R 4.0+ with DROMA.Set and DROMA.R packages
- DROMA SQLite database
Install via pip
pip install droma-mcp
Development Installation
git clone https://github.com/mugpeng/DROMA_MCP
cd DROMA_MCP
pip install -e .
R Dependencies
Ensure you have the DROMA R packages installed:
```r
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