Zeek-MCP

This repository provides a set of utilities to build an MCP server (Model Context Protocol) that you can integrate with your conversational AI client.

Table of Contents

• Prerequisites

• Installation

• Usage

  • 1. Clone the repository
  • 2. Install dependencies
  • 3. Run the MCP server
  • 4. Use the MCP tools

• Examples

• License

Examples

An example of MCP tools usage from a chainlit chatbot client, it was used an example pcap file (you can find fews in pcaps folder)

In that case the used model was claude-3.7-sonnet-reasoning-gemma3-12b

Prerequisites

• Python 3.7+
• Zeek installed and available in your PATH (for the execzeek tool)
• pip (for installing Python dependencies)

Installation

1. Clone the repository

git clone https://github.com/Gabbo01/Zeek-MCP
cd Zeek-MCP

2. Install dependencies

It's recommended to use a virtual environment:

python -m venv venv
source venv/bin/activate    # Linux/macOS
venv\Scripts\activate     # Windows
pip install -r requirements.txt

Note: If you don’t have a requirements.txt, install directly:

Copy

pip install pandas mcp

Usage

The repository exposes two main MCP tools and a command-line entry point:

3. Run the MCP server

python Bridge_Zeek_MCP.py --mcp-host 127.0.0.1 --mcp-port 8081 --transport sse

• --mcp-host: Host for the MCP server (default: 127.0.0.1).
• --mcp-port: Port for the MCP server (default: 8081).
• --transport: Transport protocol, either sse (Server-Sent Events) or stdio.

4. Use the MCP tools

You need to use an LLM that can support the MCP tools usage by calling the following tools:

1. execzeek(pcap_path: str) -> str

   • Description: Runs Zeek on the given PCAP file after deleting existing .log files in the working directory.
   • Returns: A string listing generated .log filenames or "1" on error.

2. parselogs(logfile: str) -> DataFrame

   • Description: Parses a single Zeek .log file and returns the parsed content.

You can interact with these endpoints via HTTP (if using SSE transport) or by embedding in LLM client (eg: Claude Desktop):

Claude Desktop integration:

To set up Claude Desktop as a Zeek MCP client, go to Claude -> Settings -> Developer -> Edit Config -> claude_desktop_config.json and add the following:

{
  "mcpServers": {
    "Zeek-mcp": {
      "command": "python",
      "args": [
        "/ABSOLUTE_PATH_TO/Bridge_Zeek_MCP.py",
      ]
    }
  }
}

Alternatively, edit this file directly:

/Users/YOUR_USER/Library/Application Support/Claude/claude_desktop_config.json

5ire Integration:

Another MCP client that supports multiple models on the backend is 5ire. To set up Zeek-MCP, open 5ire and go to Tools -> New and set the following configurations:

1. Tool Key: ZeekMCP
2. Name: Zeek-MCP
3. Command: python /ABSOLUTE_PATH_TO/Bridge_Zeek_MCP.py

Alternatively you can use Chainlit framework and follow the documentation to integrate the MCP server.

License

See LICENSE for more information.