This MCP server provides a Python-based Linux Common Utilities (LCU) implementation that exposes a wide range of system operations via the Model Context Protocol. It offers tools to query and manage CPU, memory, processes, storage, filesystem, hardware, network, monitoring, and log data through MCP-compatible endpoints. You can start the server and connect with either stdio or SSE transports, depending on your deployment needs. The catalog of tools includes get_cpu_info, get_memory_info, list_processes, list_disks, read_file, ping_host, monitor_resources, and many more, enabling programmatic access to detailed system information and management capabilities from your MCP client.

To use the server, run the Python module as an MCP endpoint and connect with your MCP client using the appropriate transport. The server exposes a structured set of resources and tools under the linux:// and corresponding MCP APIs, so you can perform operations, fetch status, and subscribe to monitoring data as needed. Refer to the API documentation included in the repository README for the exact tool names and their usage patterns, as well as how to navigate the available resources for system information, monitoring, and logs.

Prerequisites:

• Python 3.12+ (per badge; the project docs reference Python 3.10+; ensure compatibility with your environment)
• Linux operating system (as noted in the README)

Installation steps:

1. Clone the repository and navigate into it: git clone https://github.com/guolisen/mcp_lcu_server.git cd mcp_lcu_server

2. Create a virtual environment and activate it: python3 -m venv .venv source .venv/bin/activate

3. Install build tooling and the package (typical Python packaging flow): python -m pip install --upgrade pip setuptools wheel python -m build

   Install the built distribution (example for a wheel in dist/)

   python -m pip install dist/*

4. Run the server (example): python -m mcp_lcu_server

Configuration (optional):

• The server looks for configuration files in ./config.yaml, ./config/config.yaml, /etc/mcp-lcu-server/config.yaml, or ~/.config/mcp-lcu-server/config.yaml. You can also pass a custom path with --config when starting the server.

Tips and common notes:

• If you plan to run the server in a production environment, consider using a virtual environment isolated from the system Python, and pin your dependencies to avoid version drift.
• The configuration supports enabling and tuning monitoring intervals and security-related options. Review the provided YAML example to tailor CPU/memory, networking, and log behaviors to your environment.
• Ensure the listed configuration file locations exist or provide a --config path; otherwise the server will run with defaults.
• For transport, you can choose stdio, sse, or both depending on how you deploy (e.g., local testing via stdio or remote access via SSE).
• If you encounter module import errors, verify that the virtual environment is active and that Python path includes the package modules.
• The MCP endpoint names are shown in the README; use the mcp_lcu_server identifier to reference the server in your MCP client configuration.