Kafka MCP Server
About
A Message Context Protocol (MCP) server that integrates with Apache Kafka, providing publish and consume functionalities for LLM and agentic applications. It allows AI models to interact with Kafka topics through a standardized interface.
Details
- Author
- MCP-Mirror
- Downloads
- 321
- Categories
- Other
Jump to
- Publish messages to a configured Kafka topic
- Consume messages from a configured Kafka topic
- Supports configurable Kafka bootstrap servers, topic name, and consumer group
- Customizable tool descriptions via environment variables
- Runs over stdio or SSE transports
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Kafka MCP ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies (pip install -r requirements.txt), configure a .env file with Kafka connection settings, then run python main.py --transport stdio. Supports stdio (default) and sse transports. Can be integrated with Claude Desktop via its MCP configuration.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"kafka mcp server": {
"pavanjava_kafka_mcp_server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"pavanjava_kafka_mcp_server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}Kafka MCP Server
A Message Context Protocol (MCP) server that integrates with Apache Kafka to provide publish and consume functionalities for LLM and Agentic applications.
Overview
This project implements a server that allows AI models to interact with Kafka topics through a standardized interface. It supports:
- Publishing messages to Kafka topics
- Consuming messages from Kafka topics
Prerequisites
- Python 3.8+
- Apache Kafka instance
- Python dependencies (see Installation section)
Installation
1. Clone the repository:
git clone <repository-url>
cd <repository-directory>
2. Create a virtual environment and activate it:
python -m venv venv
source venv/bin/activate # On Windows, use: venv\Scripts\activate
3. Install the required dependencies:
pip install -r requirements.txt
If no requirements.txt exists, install the following packages:
pip install aiokafka python-dotenv pydantic-settings mcp-server
Configuration
Create a .env file in the project root with the following variables:
```
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