RMCP: Statistical Analysis through Natural Conversation

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

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 → | 🔧 Troubleshooting →

Related MCP server: Statsource MCP Server

✨ 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 →

📊 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:

# 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

# 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:

{
  "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:

# 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:

# Start local HTTP server
rmcp serve-http --host 0.0.0.0 --port 8080

# Access at: http://localhost:8080/docs

Command Line Usage

# 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:

# 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 → (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

🧪 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!

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 for detailed guidelines.

📄 License

MIT License - see LICENSE file for details.

🛠️ Quick Troubleshooting

R not found?

# macOS: brew install r
# Ubuntu: sudo apt install r-base
R --version

Missing R packages?

rmcp check-r-packages  # Check what's missing

MCP connection issues?

echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | rmcp start

📖 Need more help? Check the examples directory for working code.

🙋 Support

• 🐛 Issues: GitHub Issues

• 📖 Examples: Working examples

Ready to turn conversations into statistical insights? Install RMCP and start analyzing data through AI assistants today! 🚀

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