MCP Kali Server

Kali MCP Server is a lightweight API bridge that connects MCP Clients (e.g: Claude Desktop, 5ire) to the API server which allows excuting commands on a Linux terminal.

This allows the MCP to run terminal commands like nmap, nxc or any other tool, interact with web applications using tools like curl, wget, gobuster. And perform AI-assisted penetration testing, solving CTF web challenge in real time, helping in solving machines from HTB or THM.

My Medium Article on This Tool

👉 How MCP is Revolutionizing Offensive Security

Related MCP server: K-MCP: Kali Model Context Protocol Server

🔍 Use Case

The goal is to enable AI-driven offensive security testing by:

• Letting the MCP interact with AI endpoints like OpenAI, Claude, DeepSeek, or any other models.

• Exposing an API to execute commands on a Kali machine.

• Using AI to suggest and run terminal commands to solve CTF challenges or automate recon/exploitation tasks.

• Allowing MCP apps to send custom requests (e.g., curl, nmap, ffuf, etc.) and receive structured outputs.

Here are some example for my testing (I used google's AI gemini 2.0 flash)

Example solving my web CTF challenge in RamadanCTF

https://github.com/user-attachments/assets/dc93b71d-9a4a-4ad5-8079-2c26c04e5397

Trying to solve machine "code" from HTB

https://github.com/user-attachments/assets/3ec06ff8-0bdf-4ad5-be71-2ec490b7ee27

🚀 Features

• 🧠 AI Endpoint Integration: Connect your kali to any MCP of your liking such as claude desktop or 5ier.

• 🖥️ Command Execution API: Exposes a controlled API to execute terminal commands on your Kali Linux machine.

• 🕸️ Web Challenge Support: AI can interact with websites and APIs, capture flags via curl and any other tool AI the needs.

• 🔐 Designed for Offensive Security Professionals: Ideal for red teamers, bug bounty hunters, or CTF players automating common tasks.

🛠️ Installation and Running

On your Kali Machine

git clone https://github.com/Wh0am123/MCP-Kali-Server.git
cd MCP-Kali-Server
pip install -r requirements.txt
python3 kali_server.py

Command Line Options:

• --ip <address>: Specify the IP address to bind the server to (default: 127.0.0.1 for localhost only)

  • Use 127.0.0.1 for local connections only (secure, recommended)

  • Use 0.0.0.0 to allow connections from any network interface (very dangerous; use with caution)

  • Use a specific IP address to bind to a particular network interface

• --port <port>: Specify the port number (default: 5000)

• --debug: Enable debug mode for verbose logging

Examples:

# Run on localhost only (secure, default)
python3 kali_server.py

# Run on all interfaces (less secure, useful for remote access)
python3 kali_server.py --ip 0.0.0.0

# Run on a specific IP and custom port
python3 kali_server.py --ip 192.168.1.100 --port 8080

# Run with debug mode
python3 kali_server.py --debug

On your MCP client machine (can be local or remote)

git clone https://github.com/Wh0am123/MCP-Kali-Server.git
cd MCP-Kali-Server
pip install -r requirements.txt

If you're running the client and server on the same machine:

./mcp_server.py --server http://127.0.0.1:5000

If separate machines, create an ssh tunnel to your Kali MCP server, then launch the client:

ssh -L 5000:localhost:5000 user@KALI_IP
./mcp_server.py --server http://127.0.0.1:5000

NOTE: If you're openly hosting the Kali MCP server on your network (kali_server --IP...), you don't need the SSH tunnel ⚠️(this is highly discouraged)⚠️.

./mcp_server.py --server http://LINUX_IP:5000

Configuration for claude desktop:

edit (C:\Users\USERNAME\AppData\Roaming\Claude\claude_desktop_config.json)

{
    "mcpServers": {
        "kali_mcp": {
            "command": "python3",
            "args": [
                "/absolute/path/to/mcp_server.py",
                "--server",
                "http://LINUX_IP:5000/"
            ]
        }
    }
}

Configuration for 5ire Desktop Application:

• Simply add an MCP with the command python3 /absolute/path/to/mcp_server.py http://LINUX_IP:5000 and it will automatically generate the needed configuration files.

🔮 Other Possibilities

There are more possibilites than described since the AI model can now execute commands on the terminal. Here are some example:

• Memory forensics using Volatility

  • Automating memory analysis tasks such as process enumeration, DLL injection checks, and registry extraction from memory dumps.

• Disk forensics with SleuthKit

  • Automating analysis from disk images, timeline generation, file carving, and hash comparisons.

⚠️ Disclaimer:

This project is intended solely for educational and ethical testing purposes. Any misuse of the information or tools provided — including unauthorized access, exploitation, or malicious activity — is strictly prohibited. The author assumes no responsibility for misuse.