rmcp CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

RMCP (R Model Context Protocol) is a statistical analysis server that bridges AI assistants with R statistical computing capabilities through the Model Context Protocol.

Development Commands

Setup & Running (Hybrid Approach)

For Python-only development (cross-platform):

uv sync --group dev              # Install minimal Python dependencies
uv run rmcp start                # Start server in stdio mode (no R tools)
uv run rmcp serve-http           # Start Streamable HTTP server

# Use configuration options
uv run rmcp --debug start        # Enable debug mode
uv run rmcp --config config.json start  # Use custom config file
RMCP_LOG_LEVEL=DEBUG uv run rmcp start  # Use environment variables

# Shell completion (click provides it; zsh/bash/fish all verified)
eval "$(_RMCP_COMPLETE=zsh_source rmcp)"   # bash needs 4.4+, macOS ships 3.2

Logs go to stderr as one JSON object per line — stdout carries the JSON-RPC stream in stdio mode. Every record, whether from structlog or plain logging, gets the same envelope, and anything logged while serving a request carries request_id, so a tool call and its R execution can be tied together:

{"event": "tool completed", "tool": "linear_model", "duration_ms": 412,
 "ok": true, "request_id": "7", "component": "registries.tools", ...}

For R integration development (Docker-based):

docker build --target development -t rmcp-dev .  # Build R + Python dev environment
docker run -v $(pwd):/workspace -it rmcp-dev bash
# Inside container:
cd /workspace && pip install -e .
rmcp start                        # Full R integration available

For production deployment (optimized):

docker build --target production -t rmcp-production .  # Multi-stage optimized build
docker run -p 8000:8000 rmcp-production rmcp serve-http                          # Production HTTP server