Windows MCP Server

This project provides a Windows MCP (Model Context Protocol) server exposing useful system information and control tools for Windows environments on your AI applications.

What’s new

• More robust drive and uptime implementations (no brittle PowerShell parsing).

• Structured results for top processes (list of objects with pid, name, cpu_percent, memoryMB).

• Structured results for memory and network information.

• Safer PowerShell usage with JSON parsing for GPU info.

Related MCP server: System Info MCP Server

Features

• System info (OS, release, version, architecture, hostname)

• Uptime and last boot time

• Drives listing and per-drive space usage

• Memory, CPU, GPU, and Network information

• Top processes by memory and CPU (accurate sampling)

Available Tools

Note: Previously documented tools Windows-name-version, Windows-drives-status, and Windows-all-drives-status are not currently implemented to avoid duplication. If needed, they can be added easily.

Requirements

• Python 3.13+

• uv (for fast startup and dependency management)

# On Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Installation

1. Clone this repository:

git clone https://github.com/carlosedp/windows-mcp-server.git
cd windows-mcp-server

Running the development Server

uv run mcp dev main.py

uv handles the installation of dependencies and runs the server with the MCP protocol enabled.

Then in the MCP Inspector browser window:

• Click "Connect" to connect the MCP client.

• Go to the "Tools" tab to see available tools.

• Click "List Tools" to see the available tools.

• Select a tool and click "Run tool" to execute it.

Examples

Windows-system-info

{
  "name": "MY-PC",
  "system": "Windows",
  "release": "10",
  "version": "10.0.19045",
  "architecture": "64bit",
  "hostname": "MY-PC"
}

Windows-drive-status (input: "C")

{
  "name": "C",
  "used_spaceGB": 120.53,
  "free_spaceGB": 380.12
}

Windows-memory-info

{
  "total_memory": "32.00 GB",
  "available_memory": "18.25 GB",
  "used_memory": "13.75 GB"
}

Windows-network-info

{
  "Ethernet": "192.168.1.50",
  "Wi-Fi": "10.0.0.15"
}

Windows-top-processes-by-cpu (amount: 3)

[
  { "pid": 1234, "name": "chrome.exe", "cpu_percent": 24.7, "memoryMB": 512.3 },
  { "pid": 4321, "name": "code.exe",   "cpu_percent": 12.1, "memoryMB": 650.8 },
  { "pid": 9876, "name": "System",     "cpu_percent": 8.4,  "memoryMB": 45.0 }
]

MCP Client Configuration Example

LM Studio using Llama 3.2 3B getting system information using the MCP server:

Some more examples of tools you can run:

To connect your MCP client to this server (like Claude Desktop, VSCode, LM Studio, etc), add the following to your client configuration:

{
  "mcpServers": {
    "windows-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\Users\\Carlos Eduardo\\repos\\windows-mcp-server",
        "run",
        "mcp",
        "run",
        "main.py"
      ]
    }
  }
}

Adjust the paths above to where the project file is located.

Packaging and Distribution

To publish as a Python package:

1. Edit pyproject.toml with your metadata.

2. Build and upload to PyPI:

python -m build
python -m twine upload dist/*

License

MIT