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Exposes system information and control tools for Windows environments via MCP.
Configuration
View docs{
"mcpServers": {
"carlosedp-windows-mcp-server": {
"command": "uv",
"args": [
"run",
"mcp",
"dev",
"main.py"
]
}
}
}You run a Windows MCP Server to expose essential system data and control tools to your AI applications. It provides structured, programmatic access to OS information, resource usage, and common administrative insights, so your agents can monitor, report, and act on a Windows host with confidence.
You connect your MCP client to the Windows MCP Server to start querying tools and executing tasks. Use the Tools panel to see what capabilities are available, then run a tool to fetch data or perform an action. The server exposes a set of practical tools that return structured results you can consume directly in your AI workflows.
Prerequisites: Python 3.13+ and uv. Install uv first to enable fast startup and dependency management.
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"Clone the MCP server repository, install dependencies, and prepare to run the development server.
git clone https://github.com/carlosedp/windows-mcp-server.git
cd windows-mcp-serverStart the MCP server locally. This uses uv to run the MCP entry point in development mode.
uv run mcp dev main.pyFetches OS details including name, release, version, architecture, and hostname.
Returns the timestamp of the last system boot.
Provides the system uptime since the last boot as a readable string.
Lists all available drives by letter.
Gets used and free space for a specified drive.
Returns status for a set of drives given a comma-separated string of letters.
Reports RAM usage including total, available, and used memory.
Gathers IPv4 addresses per network interface.
Provides CPU model, logical core count, and frequency.
Returns GPU names and driver versions.
Lists top processes by memory usage with PID, name, memory usage, and optional CPU percentage.
Lists top processes by CPU usage with PID, name, CPU percentage, and memory usage.