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Kafka MCP Server

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:

# Kafka Configuration KAFKA_BOOTSTRAP_SERVERS=localhost:9092 TOPIC_NAME=your-topic-name IS_TOPIC_READ_FROM_BEGINNING=False DEFAULT_GROUP_ID_FOR_CONSUMER=kafka-mcp-group # Optional: Custom Tool Descriptions # TOOL_PUBLISH_DESCRIPTION="Custom description for the publish tool" # TOOL_CONSUME_DESCRIPTION="Custom description for the consume tool"

Usage

Running the Server

You can run the server using the provided main.py script:

python main.py --transport stdio

Available transport options:

  • stdio: Standard input/output (default)
  • sse: Server-Sent Events

Integrating with Claude Desktop

To use this Kafka MCP server with Claude Desktop, add the following configuration to your Claude Desktop configuration file:

{ "mcpServers": { "kafka": { "command": "python", "args": [ "<PATH TO PROJECTS>/main.py" ] } } }

Replace <PATH TO PROJECTS> with the absolute path to your project directory.

Project Structure

  • main.py: Entry point for the application
  • kafka.py: Kafka connector implementation
  • server.py: MCP server implementation with tools for Kafka interaction
  • settings.py: Configuration management using Pydantic

Available Tools

kafka-publish

Publishes information to the configured Kafka topic.

kafka-consume

consume information from the configured Kafka topic.

  • Note: once a message is read from the topic it can not be read again using the same groupid

Create-Topic

Creates a new Kafka topic with specified parameters.

  • Options:
    • --topicName of the topic to create
    • --partitionsNumber of partitions to allocate
    • --replication-factorReplication factor across brokers
    • --config(optional) Topic-level configuration overrides (e.g., retention.ms=604800000)

Delete-Topic

Deletes an existing Kafka topic.

  • Options:
    • --topicName of the topic to delete
    • --timeout(optional) Time to wait for deletion to complete

List-Topics

Lists all topics in the cluster (or filtered by pattern).

  • Options:
    • --bootstrap-serverBroker address
    • --pattern(optional) Regular expression to filter topic names
    • --exclude-internal(optional) Exclude internal topics (default: true)

Topic-Configuration

Displays or alters configuration for one or more topics.

  • Options:
    • --describeShow current configs for a topic
    • --alterModify configs (e.g., --add-config retention.ms=86400000,--delete-config cleanup.policy)
    • --topicName of the topic

Topic-Metadata

Retrieves metadata about a topic or the cluster.

  • Options:
    • --topic(If provided) Fetch metadata only for this topic
    • --bootstrap-serverBroker address
    • --include-offline(optional) Include brokers or partitions that are offline
-
security - not tested
A
license - permissive license
-
quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables AI models to publish and consume messages from Apache Kafka topics through a standardized interface, making it easy to integrate Kafka messaging with LLM and agent applications.

  1. Overview
    1. Prerequisites
      1. Installation
        1. Configuration
          1. Usage
            1. Running the Server
            2. Integrating with Claude Desktop
          2. Project Structure
            1. Available Tools
              1. kafka-publish
              2. kafka-consume
              3. Create-Topic
              4. Delete-Topic
              5. List-Topics
              6. Topic-Configuration
              7. Topic-Metadata

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