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MCP Kafka Processor

by boufnichel

An MCP server implementation that provides event processing capabilities through Kafka integration. This server allows Large Language Models to interact with Kafka topics through the Model Context Protocol.

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What is MCP Kafka Processor?

MCP Kafka Processor is a server that enables event processing via Kafka integration, allowing Large Language Models to interact with Kafka topics using the Model Context Protocol.

How to use MCP Kafka Processor?

To use the server, clone the repository, install dependencies using npm install, build the project with npm run build, and configure the server using environment variables such as KAFKA_BROKERS. Integrate with Cloud Desk by adding the server configuration to cloud_desktop_config.json. Available tools include publish-event and consume-events.

Key features of MCP Kafka Processor

  • Publish events to Kafka topics

  • Consume events from Kafka topics

  • Easy integration with Claude Desktop and other MCP clients

  • Type-safe implementation using TypeScript

Use cases of MCP Kafka Processor

  • Real-time data processing

  • Event-driven architectures

  • Integration with LLMs for data ingestion and analysis

  • Building scalable and reliable messaging systems

FAQ from MCP Kafka Processor

What are the prerequisites for running this server?

Node.js 16 or higher, access to a Kafka broker, and TypeScript knowledge for development.

How do I configure the Kafka brokers?

Use the KAFKA_BROKERS environment variable, providing a comma-separated list of Kafka brokers (e.g., localhost:9092).

How do I publish an event to Kafka?

Use the publish-event tool, providing the topic, message content, and optional key.

How do I consume events from Kafka?

Use the consume-events tool, specifying the topic and the maximum number of messages to consume.

How do I integrate this server with Claude Desktop?

Add the server configuration to your Claude Desktop configuration (cloud_desktop_config.json), specifying the command, arguments, and environment variables.