Here are some example (using Google's AI gemini 2.0 flash):

Example solving a web CTF challenge from 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

sudo apt install mcp-kali-server
kali-server-mcp

Otherwise for bleeding edge:

git clone https://github.com/Wh0am123/MCP-Kali-Server.git
cd MCP-Kali-Server
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
./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)
./server.py

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

# Run on a specific IP and custom port
./server.py --ip 192.168.1.100 --port 8080

# Run with debug mode
./server.py --debug

On your MCP client machine

This can be local (on the same Kali machine) or remote (another Linux machine, Windows or macOS).

If you're running the client and server on the same Kali machine (aka local), run either:

## OS package
kali-server-mcp --server http://127.0.0.1:5000

# ...OR...

## Bleeding edge
./client.py --server http://127.0.0.1:5000

If separate machines (aka remote), create an SSH tunnel to your MCP server, then launch the client:

## Terminal 1 - Replace `LINUX_IP` with Kali's IP
ssh -L 5000:localhost:5000 user@LINUX_IP

## Terminal 2
git clone https://github.com/Wh0am123/MCP-Kali-Server.git
cd MCP-Kali-Server
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
./client.py --server http://127.0.0.1:5000

If you're openly hosting the MCP Kali server on your network (server.py --IP...), you don't need the SSH tunnel (but we do recommend it!) NOTE: ⚠️(THIS IS STRONGLY DISCOURAGED. WE RECOMMEND SSH)⚠️.

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

Configuration for Claude Desktop:

Edit:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Example MCP-Kali-Server.json

Configuration for 5ire Desktop Application:

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

🔮 Other Possibilities

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

• 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.