RMCP MCP server
Commit 13 Nov 2026Menjalankan analisis statistik R, regresi, time series, dan machine learning pada data Anda melalui percakapan bahasa alami.
Juga dikenal sebagai finite-sample/rmcp
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- 13 Nov 2026
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Apa itu RMCP MCP?
RMCP adalah MCP server. Menjalankan analisis statistik R, regresi, time series, dan machine learning pada data Anda melalui percakapan bahasa alami.
Ringkasan dibuat dari README proyek
Tentang RMCP MCP server
Melakukan pemodelan statistik yang kompleks dan data science melalui bahasa alami adalah kemampuan yang ditawarkan rmcp bagi para peneliti dan analis. Dengan terhubung ke AI client seperti Claude Desktop atau menggunakan endpoint HTTP jarak jauh, pengguna dapat mengeksekusi operasi berbasis R yang canggih seperti regresi linear, peramalan time series, dan tugas machine learning tanpa perlu menulis kode.
Tool ini menonjol karena menyediakan akses ke ekosistem masif berisi 429 paket R yang dikurasi dari CRAN task views, mencakup bidang seperti ekonometrika, analisis Bayesian, dan clustering. RMCP menangani berbagai format data termasuk CSV, Excel, dan JSON, serta dapat menghasilkan visualisasi inline profesional seperti heatmap dan scatter plot langsung di dalam antarmuka chat. Untuk kebutuhan khusus, rute analisis studi opsional menyediakan referensi file yang terverifikasi konten dan adapter yang tervalidasi untuk dataset dengan konteks pengumpulan tertentu.
Apa yang bisa Anda lakukan dengannya
- 1Memprediksi tren bisnisMinta AI untuk meramalkan angka penjualan di masa depan menggunakan model time series seperti ARIMA.
- 2Mengukur efektivitas pemasaranGunakan analisis regresi untuk menentukan return on investment dari pengeluaran pemasaran tertentu.
- 3Segmentasi pelangganTerapkan algoritma machine learning untuk mengelompokkan pelanggan berdasarkan pola perilaku mereka.
- 4Validasi hasil eksperimenTentukan apakah hasil dari A/B test signifikan secara statistik menggunakan uji standar.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Konfigurasi
RMCP_API_KEYRMCP_HTTP_PORTRMCP_LOG_LEVELRMCP_R_TIMEOUT
| Nama | Deskripsi | Tipe |
|---|---|---|
| RMCP_API_KEY | — | |
| RMCP_HTTP_PORT | — | |
| RMCP_LOG_LEVEL | — | |
| RMCP_R_TIMEOUT | — |
Pengecekan otomatis
Berjalan 12 Nov 2026Laporan lengkap →Belum diverifikasi. Sebuah server memerlukan skor 80, tidak ada temuan serius, dan data yang cukup untuk kami periksa. Cara kerja checks · Yang perlu diperbaiki untuk menambah poin
- Source repository terhubung dan dapat dijangkauLolos
- Lisensi open source (OSI)Lolos
- Ada commit atau release dalam satu tahun terakhirLolos
- Nama package tidak mirip dengan package populerTidak diperiksa
- Remote endpoint menggunakan HTTPSTidak diperiksa
- Tools yang bersifat destruktif telah diberi labelTidak diperiksa
- Tidak ada instruksi tersembunyi untuk modelLolos
- Tidak ada advisory kritis pada paket atau dependensinyaTidak diperiksa
README
Lihat di GitHubRMCP: Statistical Analysis through Natural Conversation
Turn conversations into comprehensive statistical analysis - A Model Context Protocol (MCP) server with statistical tools, an optional study analysis route, 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)
MCP Endpoint: https://rmcp-server-394229601724.us-central1.run.app/mcp (bearer token required)
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 →
✨ 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
For datasets carrying collection context, the optional study analysis route adds content-verified file references and validated studyframe adapters. Existing table-based tools retain their current behavior. The study route requires the collection-context R studyframe package and its selected backends on the execution host.
Prerequisites
- Python 3.11+
- 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 (57 packages),
# Bayesian Analysis (40 packages), and more
Package Selection: Evidence-based, using CRAN task views and download statistics
Install RMCP
# Standard installation
pip install rmcp
# The Streamable HTTP transport ships in the base install.
# This extra adds pandas/openpyxl for Excel data handling.
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)
RMCP serves the MCP Streamable HTTP transport at /mcp (spec 2025-11-25),
compatible with Claude custom connectors and OpenAI's Responses API / ChatGPT
remote MCP support. Remote deployments require a bearer token.
Production Server:
Server URL: https://rmcp-server-394229601724.us-central1.run.app/mcp
Test the connection:
# Health check
curl https://rmcp-server-394229601724.us-central1.run.app/health
# Initialize MCP session (Streamable HTTP)
curl -X POST https://rmcp-server-394229601724.us-central1.run.app/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "Authorization: Bearer $RMCP_API_KEY" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"test-client","version":"1.0"}}}'
Local HTTP server:
# Localhost (no auth required)
rmcp serve-http
# Remote bind requires a bearer token (or --allow-unauthenticated)
RMCP_API_KEY=your-secret rmcp serve-http --host 0.0.0.0 --port 8080
Command Line Usage
# Start MCP server (for Claude Desktop)
rmcp start
# Start HTTP server (for web apps)
rmcp serve-http --host 0.0.0.0 --port 8080
# Start HTTPS server (production ready)
rmcp serve-http --ssl-keyfile server.key --ssl-certfile server.crt --port 8443
# Quick HTTPS setup for development
./scripts/setup/setup_https_dev.sh && source certs/https-env.sh && rmcp serve-http
# Use configuration file
rmcp --config ~/.rmcp/config.json start
# Enable debug mode
rmcp --debug start
# Check installation
rmcp --version
Shell Completion
# zsh — add to ~/.zshrc
eval "$(_RMCP_COMPLETE=zsh_source rmcp)"
# bash — add to ~/.bashrc (requires bash 4.4+)
eval "$(_RMCP_COMPLETE=bash_source rmcp)"
# fish — write to the completions directory
_RMCP_COMPLETE=fish_source rmcp > ~/.config/fish/completions/rmcp.fish
macOS ships bash 3.2, which is too old — click prints a warning and completion does nothing. Use zsh (the macOS default) or install a newer bash.
⚙️ 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 →
🔥 Key Features
- 🎯 Natural Conversation: Ask questions in plain English, get statistical analysis
- 📚 Comprehensive Package Ecosystem: 429 R packages from systematic CRAN task views
- 📊 Professional Output: Formatted results with markdown tables and inline visualizations
- 🔒 Production Ready: Official MCP SDK with stdio and Streamable HTTP transports, plus bearer-token auth for remote deployments
- ⚙️ Flexible Configuration: Environment variables, config files, and CLI options
- ⚡ Tested at the protocol boundary: deterministic semantic, malformed-data, security, approval, and recovery contracts run through the official MCP client
- 🌐 Multiple Transports: stdio (Claude Desktop) and HTTP (web applications)
- 🛡️ Guardrails: Package allowlist, explicit user approval for file writes, package installs and system calls, and filesystem confinement for tool-written files. These guard against mistakes, not adversaries — RMCP executes R as the invoking user, so run it as a trusted local tool rather than an untrusted multi-tenant service.
📚 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's evaluation guide defines package, contract, protocol, and model-level release gates. The deterministic E2E suite launches a real RMCP stdio process, connects with the official MCP client, and checks exact statistical identities alongside malformed data, code-like inputs, filesystem escape attempts, approval state, and recovery behavior.
uv run pytest tests/evals/test_mcp_server_evals.py
🤝 Contributing
We welcome contributions!
git clone https://github.com/finite-sample/rmcp.git
cd rmcp
pip install -e ".[dev]"
# Run tests
uv run pytest tests/
# Lint and format
uv run ruff check --fix .
uv run ruff format .
📄 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?
rmcp list-capabilities # verify tools register without starting a session
rmcp --debug start # run the server with verbose logging on stderr
📖 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! 🚀
Pertanyaan tentang RMCP MCP
Jenis analisis statistik apa yang bisa dijalankan oleh RMCP?
RMCP menangani regresi linear dan logistik, panel data, instrumental variables, peramalan ARIMA, clustering, decision trees, random forests, serta uji hipotesis seperti t-tests dan ANOVA.
Berapa banyak paket R yang tersedia di RMCP?
RMCP menyertakan daftar whitelist berisi 429 paket R yang diambil dari CRAN task views di lebih dari 19 kategori.
Apakah RMCP memerlukan autentikasi untuk penggunaan jarak jauh?
Ya, deployment HTTP jarak jauh memerlukan bearer token, sedangkan server localhost tidak memerlukan autentikasi.
Bagaimana cara menginstal RMCP?
Instal menggunakan pip dan jalankan rmcp start; diperlukan Python 3.11+ dan R 4.4.0+.