auto-om
A comprehensive MCP server for Linux automation operations management, providing 88 tools across 8 categories for file, system, process, network, compression, and package management via SSH connections.
README
auto-om - Linux Automation Operations Management MCP Server
A comprehensive Model Context Protocol (MCP) server for Linux automation operations management. This MCP server provides 74+ tools across 8 categories for efficient Linux server management through SSH connections.
Features
Tool Categories (88 tools total)
| Category | Tools Count | Status |
|---|---|---|
| 📁 File & Directory Management | 12 | ✅ Complete |
| 📝 Text Processing & Analysis | 11 | ✅ Complete |
| 📊 System Monitoring | 11 | ✅ Complete |
| ⚙️ Process Management | 10 | ✅ Complete |
| 👤 User & Permissions | 0 | ⏳ Pending |
| 🌐 Network Operations | 14 | ✅ Complete |
| 📦 Compression & Archive | 8 | ✅ Complete |
| 🔄 Package Management | 7 | ✅ Complete |
Key Capabilities
- File Operations: Create, copy, move, delete files and directories
- Text Processing: View, search, transform text files with regex support
- System Monitoring: CPU, memory, disk, process monitoring in real-time
- Process Control: Kill, signal, and manage process priorities
- Network Diagnostics: Ping, traceroute, DNS, HTTP testing, port scanning
- Compression: Create and extract tar, zip, gzip, bzip2, xz archives
- Package Management: Install, remove, update packages (apt, yum, dnf)
Installation
Prerequisites
- Python 3.11 or higher
- pip and pipenv
- Linux servers with SSH access
Setup from Source
# Clone the repository
git clone https://github.com/atoncooper/auto-om.git
cd auto-om
# Install dependencies with pipenv
pipenv install
# Activate virtual environment
pipenv shell
# Or install with pip directly
pip install -r requirements.txt
Using Docker
# Build the Docker image
docker build -t auto-om:latest .
# Or use docker-compose
docker-compose up -d
Configuration
Create an application.yaml file:
# Transport mode: "stdio" or "http"
transport:
mode: http
# HTTP settings (for http mode)
http:
host: 0.0.0.0
port: 8000
# SSH connection pool
ssh_pool:
default_timeout: 30
max_retries: 3
connection_timeout: 10
retry_delay: 5
max_connections: 10
# Add your Linux servers
servers:
- host: "192.168.1.100"
port: 22
username: "your_username"
password: "your_password"
alias: "server1"
⚠️ Security Warning: Use environment variables for sensitive data in production:
export SSH_PASSWORD="your_password"
Then reference it in YAML:
servers:
- host: "192.168.1.100"
username: "your_username"
password: "${SSH_PASSWORD}"
Usage
Start the Server
# Run in stdio mode (default, for MCP clients)
python main.py
# Run in HTTP mode
python main.py --mode http
# Run with custom host/port
python main.py --mode http --host 127.0.0.1 --port 9000
# Using custom config
python main.py --config /path/to/config.yaml
Docker Usage
# Run with docker-compose
docker-compose up
# Run with docker
docker run -p 8000:8000 \
-v $(pwd)/application.yaml:/app/application.yaml \
auto-om:latest
Documentation
Complete documentation for integrating with various AI development tools:
| Tool | Documentation | Use Case |
|---|---|---|
| Claude Code | Claude Code Guide | CLI-based AI development assistant |
| Cursor | Cursor Guide | AI-powered IDE for coding |
| Trae | Trae Guide | Mobile/terminal AI assistant |
Quick Start with AI Tools
Claude Code (CLI)
# Install Claude Code
npm install -g @anthropic-ai/claude-code
# Configure in ~/.config/Claude/claude_desktop_config.json
# Then use: claude-code "Check server status on server1"
Cursor (IDE)
- Open Cursor Settings
- Navigate to MCP section
- Add server:
http://localhost:8000/sse - Start using tools in Cursor's AI chat
Trae (Mobile)
- Install Trae app (iOS/Android)
- Add MCP server:
http://your-server-ip:8000/sse - Use voice or text commands for server management
Quick Start
Use the provided scripts:
# Linux/macOS
./scripts/start.sh
# Windows
scripts\start.bat
MCP Client Integration
Using Postman MCP
- Install Postman MCP Client
- Add MCP Server:
http://your-server:8000/sse - Tools will be automatically discovered
Using Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"auto-om": {
"command": "python",
"args": ["/path/to/auto-om/main.py"],
"env": {
"PYTHONPATH": "/path/to/auto-om"
}
}
}
}
Tool Examples
File Management
{
"host": "server1",
"path": "/home/user/documents"
}
System Monitoring
{
"host": "server1",
"human_readable": true
}
Process Control
{
"host": "server1",
"pid": 1234,
"signal": "TERM"
}
Network Diagnostics
{
"host": "server1",
"destination": "google.com",
"count": 5
}
Package Management
{
"host": "server1",
"packages": "nginx",
"dry_run": true
}
Architecture
auto-om/
├── main.py # Entry point
├── application.yaml # Configuration file
├── requirements.txt # Python dependencies
├── Dockerfile # Container definition
├── docker-compose.yml # Multi-container setup
├── src/
│ ├── core/ # Core MCP functionality
│ │ ├── server.py # MCP server implementation
│ │ └── ssh_client.py # SSH connection pool
│ └── tools/ # Tool implementations
│ ├── file_mgr.py # File management (12 tools)
│ ├── text_proc.py # Text processing (11 tools)
│ ├── system_monitor.py # System monitoring (11 tools)
│ ├── process_mgr.py # Process management (10 tools)
│ ├── network_ops.py # Network operations (14 tools)
│ ├── compress_ops.py # Compression (8 tools)
│ └── package_mgr.py # Package management (7 tools)
├── VERSION.md # Version history
└── README.md # This file
Security Features
- Non-root Operations: All operations designed for non-root users
- Path Validation: Prohibited operations on system directories
- Command Validation: Dangerous commands are blocked
- Dry-run Mode: Preview destructive operations before executing
- Connection Pooling: Secure SSH connection management
- Output Limits: Prevent overwhelming responses
Supported Linux Distributions
- Debian 10+
- Ubuntu 18.04+
- CentOS 7+
- RHEL 7+
- Fedora 30+
Dependencies
- Python 3.11+
- paramiko 3.5.0 (SSH2 protocol)
- mcp 1.25.0 (Model Context Protocol)
- pyyaml 6.0.3 (YAML configuration)
Development
# Install development dependencies
pipenv install --dev
# Run tests (when available)
pytest tests/
# Format code
black src/
# Type checking
mypy src/
Version History
See VERSION.md for detailed version history.
Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Support
For issues and questions:
- GitHub Issues: https://github.com/atoncooper/auto-om/issues
- Documentation: VERSION.md
Acknowledgments
- Built with MCP Python SDK
- Inspired by common Linux automation tools
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。