Back to Skills

containerize-mcp-server

pjt222
Updated 2 days ago
2 views
17
2
17
View on GitHub
Metaaimcpdesign

About

This skill enables developers to containerize R-based MCP servers using Docker, eliminating the need for local R installations. It covers mcptools integration, port configuration, and transport methods (stdio/HTTP) for creating reproducible environments. Use it for deploying portable MCP servers alongside other services or distributing them to other developers.

Quick Install

Claude Code

Recommended
Primary
npx skills add pjt222/agent-almanac -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternative
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/containerize-mcp-server

Copy and paste this command in Claude Code to install this skill

Documentation

Containerize MCP Server

Package R MCP server into Docker container for portable deployment.

When Use

  • Deploy R MCP server without requiring local R installation
  • Create reproducible MCP server environment
  • Run MCP servers alongside other containerized services
  • Distribute MCP server to other developers

Inputs

  • Required: R MCP server implementation (mcptools-based or custom)
  • Required: Docker installed, running
  • Optional: Additional R packages server needs
  • Optional: Transport mode (stdio or HTTP)

Steps

Step 1: Create Dockerfile for MCP Server

FROM rocker/r-ver:4.5.0

# Install system dependencies
RUN apt-get update && apt-get install -y \
    libcurl4-openssl-dev \
    libssl-dev \
    libxml2-dev \
    libgit2-dev \
    libssh2-1-dev \
    git \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Install R packages
RUN R -e "install.packages(c( \
    'remotes', \
    'ellmer' \
    ), repos='https://cloud.r-project.org/')"

# Install mcptools
RUN R -e "remotes::install_github('posit-dev/mcptools')"

# Set working directory
WORKDIR /workspace

# Expose MCP server ports
EXPOSE 3000 3001 3002

# Environment variables
ENV R_LIBS_USER=/workspace/renv/library
ENV RENV_PATHS_CACHE=/workspace/renv/cache

# Default: start MCP server
CMD ["R", "-e", "mcptools::mcp_server()"]

Got: Dockerfile exists in project root with rocker/r-ver base image, system dependencies, mcptools installation, MCP server as default command.

If fail: Verify base image tag matches your R version. remotes::install_github fails? Check git and libgit2-dev are in system dependencies layer.

Step 2: Create docker-compose.yml

version: '3.8'

services:
  mcp-server:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: r-mcp-server
    image: r-mcp-server:latest

    volumes:
      - /path/to/projects:/workspace
      - renv-cache:/workspace/renv/cache

    stdin_open: true
    tty: true

    network_mode: "host"

    environment:
      - TERM=xterm-256color
      - R_LIBS_USER=/workspace/renv/library

    restart: unless-stopped

volumes:
  renv-cache:
    driver: local

Using network_mode: "host" ensures MCP server ports accessible on localhost.

Got: docker-compose.yml in project root with MCP server service, volume mounts for project files and renv cache, stdin_open/tty enabled for stdio transport.

If fail: Volume paths invalid? Adjust /path/to/projects to actual project directory. On Windows/WSL, use /mnt/c/... or /mnt/d/... paths.

Step 3: Build and Start

docker compose build
docker compose up -d

Got: Container starts with MCP server running.

If fail: Check logs with docker compose logs mcp-server. Common issues:

  • Missing R packages: Add to Dockerfile RUN install step
  • Port already in use: Change exposed port or stop conflicting service

Step 4: Connect Claude Code to Container

For stdio transport (container must stay running with stdin):

claude mcp add r-mcp-docker stdio "docker" "exec" "-i" "r-mcp-server" "R" "-e" "mcptools::mcp_server()"

For HTTP transport (if MCP server supports it):

{
  "mcpServers": {
    "r-mcp-docker": {
      "type": "http",
      "url": "http://localhost:3000/mcp"
    }
  }
}

Got: Claude Code MCP configuration includes r-mcp-docker server entry. claude mcp list shows new server.

If fail: Stdio transport? Ensure container name matches (r-mcp-server) and container running with docker ps. HTTP transport? Verify port exposed, reachable with curl http://localhost:3000/mcp.

Step 5: Verify Connection

# Check container is running
docker ps | grep mcp-server

# Test R session inside container
docker exec -it r-mcp-server R -e "sessionInfo()"

# Verify mcptools is available
docker exec -it r-mcp-server R -e "library(mcptools)"

Got: docker ps shows r-mcp-server container running. sessionInfo() returns expected R version. library(mcptools) loads without error.

If fail: Container not running? Check docker compose logs mcp-server for startup errors. mcptools fails to load? Rebuild image to ensure package installed correctly.

Step 6: Add Custom MCP Tools

To add project-specific MCP tools, mount R scripts:

volumes:
  - ./mcp-tools:/mcp-tools

Load them in CMD:

CMD ["R", "-e", "source('/mcp-tools/custom_tools.R'); mcptools::mcp_server()"]

Got: Custom R scripts accessible inside container at /mcp-tools/. MCP server loads them on startup alongside default tools.

If fail: Verify volume mount path correct with docker exec -it r-mcp-server ls /mcp-tools/. Scripts fail to source? Check missing package dependencies in custom tools.

Checks

  • Container builds without errors
  • MCP server starts inside container
  • Claude Code can connect to containerized server
  • MCP tools respond correctly to requests
  • Container restarts cleanly
  • Volume mounts allow access to project files

Pitfalls

  • stdin/tty requirements: MCP stdio transport requires stdin_open: true and tty: true
  • Network isolation: Default Docker networking may prevent localhost access. Use network_mode: "host" or expose specific ports.
  • Package versions: Pin mcptools to specific commit for reproducibility
  • Large image size: mcptools + dependencies can be large. Consider multi-stage builds for production.
  • Windows Docker paths: Running Docker Desktop on Windows with WSL? Path mapping differs

See Also

  • create-r-dockerfile - base Dockerfile patterns for R
  • setup-docker-compose - compose configuration details
  • configure-mcp-server - MCP server configuration without Docker
  • troubleshoot-mcp-connection - debugging MCP connectivity issues

GitHub Repository

pjt222/agent-almanac
Path: i18n/caveman/skills/containerize-mcp-server
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Related Skills

content-collections

Meta

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

View skill

polymarket

Meta

This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.

View skill

creating-opencode-plugins

Meta

This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill