Kafka MCP Server

@pavanjava

リポジトリ

基本情報

カテゴリ

その他

ライセンス

Apache-2.0

トランスポート

stdio

作者

pavanjava

Kafka MCP Server について

概要はまだありません

Kafka MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Kafka MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is 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. It allows AI models to interact with Kafka topics through a standardized interface.

How to use Kafka MCP Server?

Set up Python 3.8+, an Apache Kafka instance, and install dependencies (aiokafka, python-dotenv, pydantic-settings, mcp-server). Create a .env file with Kafka configuration (bootstrap servers, topic name, etc.). Run the server via python main.py --transport stdio or --transport sse. Integrate with Claude Desktop by adding the server command to the MCP configuration JSON.

Key features of Kafka MCP Server

Publish messages to configured Kafka topics
Consume messages from Kafka topics
Create new Kafka topics with custom partitions and replication
Delete existing Kafka topics
List all topics or filter by pattern
Display or alter topic configurations
Retrieve topic metadata

Use cases of Kafka MCP Server

An AI agent publishing analysis results or alerts to a Kafka topic
An LLM consuming messages from a topic to generate summaries or responses
Programmatic topic lifecycle management (create, delete, list) via MCP tools
Modifying topic configuration (e.g., retention) without leaving the assistant interface
Inspecting cluster metadata to debug or monitor Kafka infrastructure

よくある質問

What are the prerequisites for using Kafka MCP Server?

You need Python 3.8+, a running Apache Kafka instance, and the required Python packages (`aiokafka`, `python-dotenv`, `pydantic-settings`, `mcp-server`).

How do I configure Kafka connection settings?

Create a `.env` file in the project root with variables like `KAFKA_BOOTSTRAP_SERVERS` (default `localhost:9092`), `TOPIC_NAME`, and optionally `IS_TOPIC_READ_FROM_BEGINNING` and `DEFAULT_GROUP_ID_FOR_CONSUMER`.

Can I customize the tool descriptions shown to the LLM?

Yes. You can set optional environment variables `TOOL_PUBLISH_DESCRIPTION` and `TOOL_CONSUME_DESCRIPTION` in the `.env` file to override the default descriptions.

What transport options are available for running the server?

The server supports `stdio` (standard input/output) as the default transport and `sse` (Server-Sent Events) as an alternative, specified via the `--transport` argument.

Can a consumed message be read again?

No. The README notes that once a message is read from the topic using the same consumer group ID, it cannot be read again.

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