Which integrations are available for this server?

Provides containerized deployment of the R Econometrics MCP server, allowing for easier setup and isolation of the required R environment and dependencies. Uses Python as the interface language for the MCP server that connects R's econometric capabilities to AI assistants.

How do I use R Econometrics MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@R Econometrics MCP Server run a linear regression to see if marketing spend predicts sales revenue" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RMCP: Statistical Analysis through Natural Conversation

Python application PyPI version Downloads Documentation 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

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.

🎯 | 🔧

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"

👉

📊 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

📖 (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	Copy-paste ready examples with real data
Economic Research Examples	Panel data, time series, advanced econometrics
Time Series Examples	ARIMA, forecasting, decomposition
Image Display Examples	Inline visualizations in Claude
API Documentation	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!

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! 🚀

R Econometrics MCP Server