Content
# 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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