devops-mcp-server

ACNet-AI/devops-mcp-server

3.2

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The DevOps MCP Server enables universal DevOps automation through natural language conversation, allowing users to deploy, monitor, and manage applications with AI assistants.

Tools
15
Resources
0
Prompts
0

🚀 DevOps MCP Server

Universal DevOps automation through conversation. Deploy, monitor, and manage applications using natural language with AI assistants.

✨ Features

🐳 Docker & Docker Compose

  • Container Management - Start, stop, restart, and monitor containers
  • Compose Operations - Manage multi-container applications
  • Log Viewing - Stream and search container logs
  • Resource Monitoring - Track CPU, memory, and network usage

📊 Process Monitoring

  • Service Status - Check if services are running
  • System Info - View memory, disk, and CPU usage
  • Port Management - List and check port usage

🚂 PaaS Platform Deployment

  • Railway - Deploy to Railway with one command
  • Render - Deploy web services and databases to Render
  • Fly.io - Deploy to edge locations worldwide

📦 Container Registry

  • Docker Hub - Build, tag, and push images to Docker Hub
  • Multi-platform builds - Build for AMD64 and ARM64

🎯 Available Tools

Docker Compose (5 tools)
  • docker_compose_up - Start services
  • docker_compose_down - Stop and remove services
  • docker_compose_logs - View service logs
  • docker_compose_ps - List service status
  • docker_compose_restart - Restart services
Docker Containers (7 tools)
  • docker_ps - List containers
  • docker_logs - View container logs
  • docker_start - Start a container
  • docker_stop - Stop a container
  • docker_restart - Restart a container
  • docker_inspect - Get container details
  • docker_stats - View resource usage
Process Monitoring (3 tools)
  • check_service_status - Check if a service is running
  • get_system_info - Get system information
  • list_ports_in_use - List ports in use
Universal Deployment (8 tools) - Works for any application
  • clone_from_git - Clone from Git (GitHub/GitLab/Gitea/Bitbucket)
  • deploy_from_docker - Deploy from Docker image (any containerized app)
  • deploy_from_pypi - Install from PyPI (Python applications)
  • deploy_from_npm - Install from NPM (Node.js applications)
  • deploy_from_local - Deploy from local directory
  • install_dependencies - Auto-detect and install dependencies
  • deploy_mcp_server - Automated MCP deployment workflow
  • check_mcp_server_health - Verify MCP server health
Railway Deployment (8 tools) 🆕
  • railway_login - Authenticate with Railway
  • railway_init - Initialize new project
  • railway_link - Link to existing project
  • railway_deploy - Deploy to Railway
  • railway_status - Check deployment status
  • railway_logs - View application logs
  • railway_env_set - Set environment variables
  • railway_env_list - List environment variables
Render Deployment (6 tools) 🆕
  • render_login - Authenticate with Render
  • render_create_service - Create new service
  • render_deploy - Deploy or redeploy service
  • render_logs - View service logs
  • render_env_set - Set environment variables
  • render_list_services - List all services
Fly.io Deployment (8 tools) 🆕
  • flyio_auth_login - Authenticate with Fly.io
  • flyio_launch - Initialize new application
  • flyio_deploy - Deploy application
  • flyio_status - Check application status
  • flyio_logs - View application logs
  • flyio_scale - Scale instances or VM size
  • flyio_secrets_set - Set secrets (env vars)
  • flyio_secrets_list - List all secrets
Docker Registry (7 tools) 🆕
  • docker_login - Login to Docker Hub or registry
  • docker_build_image - Build image from Dockerfile
  • docker_tag_image - Tag image with new name
  • docker_push_image - Push image to registry
  • docker_build_and_push - Build and push in one command
  • docker_list_local_images - List local images
  • docker_remove_image - Remove local image

Total: 52 DevOps tools ready to use!

Key Architecture:

  • 🎯 Generic First: Most tools work for any application (web servers, databases, MCP)
  • 🔌 Smart Detection: Auto-detect project type and use appropriate tools
  • 🌍 Platform Agnostic: Deploy anywhere (local, remote, PaaS, containers)

Quick Start

# Install dependencies
uv sync

# Start server
uv run python server.py

Configuration

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "devops-mcp-server": {
      "command": "uv",
      "args": ["run", "python", "server.py"],
      "cwd": "/path/to/devops-mcp-server"
    }
  }
}

Project Structure

devops-mcp-server/
├── config.yaml          # Server configuration
├── pyproject.toml       # Python project configuration
├── server.py            # Server entry file
├── README.md            # Documentation
├── CHANGELOG.md         # Version change log
├── .env                 # Environment variables configuration
├── .gitignore           # Git ignore file
├── Dockerfile           # Docker container configuration
├── .dockerignore        # Docker build ignore file
├── docker-compose.yml   # Docker Compose orchestration
├── tools/               # Tools implementation directory
├── resources/           # Resources implementation directory
└── prompts/             # Prompt template directory

💬 Usage Examples

Once connected to Claude Desktop or another MCP client, you can interact naturally:

Container Management:

  • "Start my docker compose services"
  • "Show me the logs for my-app container"
  • "What containers are currently running?"
  • "Restart the database container"

Monitoring:

  • "Is nginx running?"
  • "What's using port 8080?"
  • "Show me system resource usage"

Docker Compose:

  • "Bring up my application stack"
  • "Show me the status of all services"
  • "View the logs for the web service"

PaaS Deployment:

  • "Deploy my app to Railway"
  • "Check the status of my Railway deployment"
  • "Push this Docker image to Docker Hub"
  • "Deploy my MCP server to Fly.io"

Container Registry:

  • "Build and push my-app:v1.0 to Docker Hub"
  • "Show me all my local Docker images"
  • "Tag my image for production"

🛠️ Development

The server automatically discovers tools from the tools/ directory structure:

tools/
├── docker/
│   ├── compose.py      # Docker Compose tools
│   └── container.py    # Container management tools
├── process/
│   └── monitor.py      # Process monitoring tools
├── deployment/
│   └── mcp_deploy.py   # MCP server deployment
├── paas/
│   ├── railway.py      # Railway deployment
│   ├── render.py       # Render deployment
│   └── flyio.py        # Fly.io deployment
└── registry/
    └── dockerhub.py    # Docker Hub operations

To add new tools, create Python files in the appropriate directory with functions decorated with @tool()

Deployment

Docker Deployment (Recommended)

Using docker-compose (Easiest)
# 1. Start the service
docker-compose up -d

# 2. View logs
docker-compose logs -f

# 3. Stop the service
docker-compose down
Using Docker commands
# Build image
docker build -t devops-mcp-server .

# Run container
docker run -d \
  --name devops-mcp-server \
  --env-file .env \
  -v $(pwd)/data:/app/data \
  -v $(pwd)/logs:/app/logs \
  devops-mcp-server

Local Development

# Install dependencies
uv sync

# Run server
uv run python server.py

Production Deployment

For production deployment, ensure you:

  1. Configure environment variables in .env
  2. Set up proper logging and monitoring
  3. Use restart: unless-stopped in docker-compose (already configured)
  4. Mount persistent volumes for data

Health Check

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

# View container logs
docker logs devops-mcp-server

# Check service status with docker-compose
docker-compose ps

Advanced Usage

For advanced features and examples, see the in the mcp-factory repository.

Generated by MCP Factory