Zamiel01/kali-MCP-lmstudio-integration
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The Model Context Protocol (MCP) server is a framework designed to facilitate the integration and execution of AI models across different platforms, providing a seamless interface for model deployment and management.
How I Connected Kali MCP Server to LM Studio to Run AI Models
Step 1: Install the MCP Kali Server
First, open your terminal and install the Kali MCP server package:
sudo apt install mcp-kali-server
Step 2: Clone the MCP Server Repository
Next, go to your home directory and clone the official GitHub repository to get mcp_server.py:
cd ~
git clone https://github.com/Wh0am123/MCP-Kali-Server
This will download the repository containing mcp_server.py.
Step 3: Install LM Studio
Go to the official LM Studio website and download the latest version.
Once downloaded, make it executable and run it:
chmod +x ./LM-studio-<version>-x64.AppImage
./LM-studio-<version>-x64.AppImage
Step 4: Download a Model
Once LM Studio is running, go to the Program tab (top‑left corner).
Click Install and choose a model – I recommend using the GPT‑OSS model.
Step 5: Start the Kali MCP Server
Open a new terminal window and start the server:
kali-server-mcp
You should see the server running at:
http://127.0.0.1:5000
This is your Kali MCP server.
Step 6: Configure LM Studio to Use the MCP Server
In LM Studio, open the mcp.json file.
Replace its content with the following, making sure to update the path to mcp_server.py:
{
"mcpServers": {
"kali_mcp": {
"command": "python3",
"args": [
"/absolute/path/to/mcp_server.py",
"--server",
"http://127.0.0.1:5000/"
]
}
}
}
Important: Replace /absolute/path/to/mcp_server.py with the actual path where you cloned the GitHub repository.
Save the file.
Step 7: Restart LM Studio
Close LM Studio and open it again.
Your setup should now be fully functional, allowing you to run AI models from your Kali MCP server through LM Studio.
✅ Conclusion
By connecting LM Studio to my Kali MCP server, I can now run AI models locally on Kali with full integration. This setup opens up new possibilities for testing and deploying models in a secure Linux environment.