Kafka MCP Server

@MCP-Mirror

About Kafka MCP Server

Mirror of

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "pavanjava_kafka_mcp_server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Kafka MCP Server?

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.

How to use Kafka MCP Server?

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.

Key features of Kafka MCP Server

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

Use cases of Kafka MCP Server

Enable an LLM to publish structured data to a Kafka topic
Allow an AI agent to consume and process messages from a Kafka stream
Integrate Kafka messaging into a Claude Desktop workflow

FAQ from Kafka MCP Server

What are the prerequisites?

Python 3.8+, a running Apache Kafka instance, and installation of dependencies aiokafka, python-dotenv, pydantic-settings, and mcp-server.

How do I configure the server?

Create a .env file in the project root with KAFKA_BOOTSTRAP_SERVERS, TOPIC_NAME, IS_TOPIC_READ_FROM_BEGINNING, and DEFAULT_GROUP_ID_FOR_CONSUMER. Optional TOOL_PUBLISH_DESCRIPTION and TOOL_CONSUME_DESCRIPTION can override tool descriptions.

What transport options are available?

The server supports stdio (standard input/output) and sse (Server-Sent Events). Use the --transport flag when running main.py.

Can I customize the tool names or descriptions?

Yes, by setting TOOL_PUBLISH_DESCRIPTION and TOOL_CONSUME_DESCRIPTION in the .env file you can provide custom descriptions for the publish and consume tools.

Are consumed messages re-readable?

No. Once a message is read from the topic it cannot be read again using the same consumer group. The server uses a configurable consumer group (DEFAULT_GROUP_ID_FOR_CONSUMER).

Frequently asked questions

What are the prerequisites?

Python 3.8+, a running Apache Kafka instance, and installation of dependencies `aiokafka`, `python-dotenv`, `pydantic-settings`, and `mcp-server`.

How do I configure the server?

Create a `.env` file in the project root with `KAFKA_BOOTSTRAP_SERVERS`, `TOPIC_NAME`, `IS_TOPIC_READ_FROM_BEGINNING`, and `DEFAULT_GROUP_ID_FOR_CONSUMER`. Optional `TOOL_PUBLISH_DESCRIPTION` and `TOOL_CONSUME_DESCRIPTION` can override tool descriptions.

What transport options are available?

The server supports `stdio` (standard input/output) and `sse` (Server-Sent Events). Use the `--transport` flag when running `main.py`.

Can I customize the tool names or descriptions?

Yes, by setting `TOOL_PUBLISH_DESCRIPTION` and `TOOL_CONSUME_DESCRIPTION` in the `.env` file you can provide custom descriptions for the publish and consume tools.

Are consumed messages re-readable?

No. Once a message is read from the topic it cannot be read again using the same consumer group. The server uses a configurable consumer group (`DEFAULT_GROUP_ID_FOR_CONSUMER`).

Comments

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