wklee610/kafka-mcp

Overview

wklee610/kafka-mcp is an MCP server that provides an interface to interact with Apache Kafka. It allows AI agents or tools to send and receive messages from Kafka topics using the Model Context Protocol. The implementation is in Python.

Best for

Best for
Developers building AI agent workflows that need a lightweight Kafka interface via MCP

Use cases

• Connecting AI agents to Kafka streams for real-time data processing
• Producing and consuming messages programmatically via MCP-compatible tools
• Integrating Kafka with MCP workflows for event-driven agent interactions

How to use

Install

uv sync

Tools exposed

• describe_cluster
• describe_brokers
• list_topics
• create_topic
• delete_topic
• describe_topic
• create_partitions
• describe_configs
• alter_configs
• list_consumer_groups
• describe_consumer_group
• get_consumer_group_offsets
• reset_consumer_group_offset
• rewind_consumer_group_offset_by_timestamp
• consume_messages
• produce_message

Tested with

Claude Desktop

Example client config

{\n  "mcpServers": {\n    "kafka": {\n      "command": "<uv PATH>",\n      "args": [\n        "--directory",\n        "<kafka-mcp PATH>",\n        "run",\n        "kafka-mcp"\n      ],\n      "env": {\n        "KAFKA_BOOTSTRAP_SERVERS": "localhost:9092"\n      }\n    }\n  }\n}

Notes

wklee610/kafka-mcp is an MCP server that provides an interface to interact with Apache Kafka. It allows AI agents or tools to send and receive messages from Kafka topics using the Model Context Protocol. The implementation is in Python.

11 stars on GitHub. Last updated 2026-03-08. Licensed Apache-2.0.

Use cases

• Connecting AI agents to Kafka streams for real-time data processing
• Producing and consuming messages programmatically via MCP-compatible tools
• Integrating Kafka with MCP workflows for event-driven agent interactions

Pros

• Lightweight Python implementation with simple setup
• Provides a standardized MCP interface for Kafka
• Open source and easy to customize

Cons

• Low star count suggests limited adoption or early-stage development
• May lack comprehensive documentation or community support
• Only covers basic Kafka operations; advanced features are not guaranteed

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