R Econometrics MCP Server

# RMCP: Statistical Analysis through Natural Conversation [](https://github.com/finite-sample/rmcp/actions/workflows/ci.yml) [](https://pypi.org/project/rmcp/) [](https://pepy.tech/project/rmcp) [](https://finite-sample.github.io/rmcp/) [](https://github.com/finite-sample/rmcp/blob/main/LICENSE) **Turn conversations into comprehensive statistical analysis** - A Model Context Protocol (MCP) server with **52 statistical analysis tools** across 11 categories and **429 R packages** from systematic CRAN task views. RMCP enables AI assistants to perform sophisticated statistical modeling, econometric analysis, machine learning, time series analysis, and data science tasks through natural conversation. ## 🚀 Quick Start (30 seconds) ### 🌐 **Try the Live Server** (No Installation Required) **HTTP Server**: `https://rmcp-server-394229601724.us-central1.run.app/mcp` **Interactive Docs**: `https://rmcp-server-394229601724.us-central1.run.app/docs` **Health Check**: `https://rmcp-server-394229601724.us-central1.run.app/health` ### 🖥️ **Or Install Locally** ```bash pip install rmcp rmcp start ``` That's it! RMCP is now ready to handle statistical analysis requests via Claude Desktop, Claude web, or any MCP client. **🎯 [Working examples →](examples/quick_start_guide.md)** | **🔧 [Troubleshooting →](#-quick-troubleshooting)** ## ✨ What Can RMCP Do? ### 📊 **Regression & Economics** Linear regression, logistic models, panel data, instrumental variables → *"Analyze ROI of marketing spend"* ### ⏰ **Time Series & Forecasting** ARIMA models, decomposition, stationarity testing → *"Forecast next quarter's sales"* ### 🧠 **Machine Learning** Clustering, decision trees, random forests → *"Segment customers by behavior"* ### 📈 **Statistical Testing** T-tests, ANOVA, chi-square, normality tests → *"Is my A/B test significant?"* ### 📋 **Data Analysis** Descriptive stats, outlier detection, correlation analysis → *"Summarize this dataset"* ### 🔄 **Data Transformation** Standardization, winsorization, lag/lead variables → *"Prepare data for modeling"* ### 📊 **Professional Visualizations** Inline plots in Claude: scatter plots, histograms, heatmaps → *"Show me a correlation matrix"* ### 📁 **Smart File Operations** CSV, Excel, JSON import with validation → *"Load and analyze my sales data"* ### 🤖 **Natural Language Features** Formula building, error recovery, example datasets → *"Help me build a regression formula"* **👉 [See working examples →](examples/quick_start_guide.md)** ## 📊 Real Usage with Claude ### Business Analysis **You:** *"I have sales data and marketing spend. Can you analyze the ROI?"* **Claude:** *"I'll run a regression analysis to measure marketing effectiveness..."* **Result:** *"Every $1 spent on marketing generates $4.70 in sales. The relationship is highly significant (p < 0.001) with R² = 0.979"* ### Economic Research **You:** *"Test if GDP growth and unemployment follow Okun's Law using my country data"* **Claude:** *"I'll analyze the correlation between GDP growth and unemployment..."* **Result:** *"Strong support for Okun's Law: correlation r = -0.944. Higher GDP growth significantly reduces unemployment."* ### Customer Analytics **You:** *"Predict customer churn using tenure and monthly charges"* **Claude:** *"I'll build a logistic regression model for churn prediction..."* **Result:** *"Model achieves 100% accuracy. Each additional month of tenure reduces churn risk by 11.3%. Higher charges increase churn risk by 3% per dollar."* ## 📦 Installation ### Prerequisites - **Python 3.10+** - **R 4.4.0+** with **comprehensive package ecosystem**: RMCP uses a systematic 429-package whitelist from CRAN task views organized into 19+ categories: ```r # Core packages (install these first) install.packages(c( "jsonlite", "dplyr", "ggplot2", "broom", "plm", "forecast", "randomForest", "rpart", "caret", "AER", "vars", "mgcv" )) # Full ecosystem automatically available: Machine Learning (61 packages), # Econometrics (55 packages), Time Series (48 packages), # Bayesian Analysis (32 packages), and more ``` **Package Selection**: Evidence-based using CRAN task views, download statistics, and 4-tier security assessment ### Install RMCP ```bash # Standard installation pip install rmcp # With HTTP transport support pip install rmcp[http] # Development installation git clone https://github.com/finite-sample/rmcp.git cd rmcp pip install -e ".[dev]" ``` ### Claude Desktop Integration Add to your Claude Desktop MCP configuration: ```json { "mcpServers": { "rmcp": { "command": "rmcp", "args": ["start"] } } } ``` ### HTTP Server Integration (Claude Web) **Production Server** (ready to use): ``` Server URL: https://rmcp-server-394229601724.us-central1.run.app/mcp Interactive Docs: https://rmcp-server-394229601724.us-central1.run.app/docs ``` **Test the connection**: ```bash # Health check curl https://rmcp-server-394229601724.us-central1.run.app/health # Initialize MCP session curl -X POST https://rmcp-server-394229601724.us-central1.run.app/mcp \ -H "Content-Type: application/json" \ -H "MCP-Protocol-Version: 2025-06-18" \ -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"test-client","version":"1.0"}}}' ``` **Local HTTP server**: ```bash # Start local HTTP server rmcp serve-http --host 0.0.0.0 --port 8080 # Access at: http://localhost:8080/docs ``` ### Command Line Usage ```bash # Start MCP server (for Claude Desktop) rmcp start # Start HTTP server (for web apps) rmcp serve-http --host 0.0.0.0 --port 8080 # Start HTTPS server (production ready) rmcp serve-http --ssl-keyfile server.key --ssl-certfile server.crt --port 8443 # Quick HTTPS setup for development ./scripts/setup/setup_https_dev.sh && source certs/https-env.sh && rmcp serve-http # Use configuration file rmcp --config ~/.rmcp/config.json start # Enable debug mode rmcp --debug start # Check installation rmcp --version ``` ### ⚙️ Configuration RMCP supports flexible configuration through environment variables, configuration files, and command-line options: ```bash # Environment variables export RMCP_HTTP_PORT=9000 export RMCP_R_TIMEOUT=180 export RMCP_LOG_LEVEL=DEBUG rmcp start # Configuration file (~/.rmcp/config.json) { "http": {"port": 9000}, "r": {"timeout": 180}, "logging": {"level": "DEBUG"} } # Docker with environment variables docker run -e RMCP_HTTP_HOST=0.0.0.0 -e RMCP_HTTP_PORT=8000 rmcp:latest ``` **📖 [Complete Configuration Guide →](docs/configuration/index.rst)** (auto-generated from code) ## 🔥 Key Features - **🎯 Natural Conversation**: Ask questions in plain English, get statistical analysis - **📚 Comprehensive Package Ecosystem**: 429 R packages from systematic CRAN task views with 4-tier security system - **📊 Professional Output**: Formatted results with markdown tables and inline visualizations - **🔒 Production Ready**: Full MCP protocol compliance with HTTP transport and SSE - **⚙️ Flexible Configuration**: Environment variables, config files, and CLI options - **⚡ Fast & Reliable**: 100% test success rate across all scenarios - **🌐 Multiple Transports**: stdio (Claude Desktop) and HTTP (web applications) - **🛡️ Secure**: Evidence-based package selection with security-conscious permission tiers ## 📚 Documentation | Resource | Description | |----------|-------------| | **[Quick Start Guide](examples/quick_start_guide.md)** | Copy-paste ready examples with real data | | **[Economic Research Examples](examples/economic_research_example.md)** | Panel data, time series, advanced econometrics | | **[Time Series Examples](examples/advanced_time_series_example.md)** | ARIMA, forecasting, decomposition | | **[Image Display Examples](examples/image_display_example.md)** | Inline visualizations in Claude | | **[API Documentation](docs/)** | Auto-generated API reference | ## 🧪 Validation RMCP has been tested with real-world scenarios achieving **100% success rate**: - ✅ **Business Analysts**: Sales forecasting with 97.9% R², $4.70 ROI per marketing dollar - ✅ **Economists**: Macroeconomic analysis confirming Okun's Law (r=-0.944) - ✅ **Data Scientists**: Customer churn prediction with 100% accuracy - ✅ **Researchers**: Treatment effect analysis with significant results (p<0.001) ## 🤝 Contributing We welcome contributions! ```bash git clone https://github.com/finite-sample/rmcp.git cd rmcp pip install -e ".[dev]" # Run tests python tests/unit/test_new_tools.py python tests/e2e/test_claude_desktop_scenarios.py # Format code black rmcp/ ``` See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines. ## 📄 License MIT License - see [LICENSE](LICENSE) file for details. ## 🛠️ Quick Troubleshooting **R not found?** ```bash # macOS: brew install r # Ubuntu: sudo apt install r-base R --version ``` **Missing R packages?** ```bash rmcp check-r-packages # Check what's missing ``` **MCP connection issues?** ```bash echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | rmcp start ``` **📖 Need more help?** Check the [examples](examples/) directory for working code. ## 🙋 Support - 🐛 **Issues**: [GitHub Issues](https://github.com/finite-sample/rmcp/issues) - 📖 **Examples**: [Working examples](examples/quick_start_guide.md) --- **Ready to turn conversations into statistical insights?** Install RMCP and start analyzing data through AI assistants today! 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