Akhq MCP Server

Overview

The Akhq‑MCP Server bridges the gap between AI assistants and Kafka monitoring by exposing AKHQ’s rich, real‑time data through the Model Context Protocol. For developers who rely on AI assistants like Claude or Cursor to augment their Kafka workflows, this server offers a seamless way for the assistant to query topic metrics, view consumer group lag, and inspect cluster health without leaving the AI interface. By turning AKHQ’s dashboard into an MCP endpoint, the server removes the need for manual data exports or custom API wrappers, allowing assistants to provide instant, context‑aware insights directly in the chat window.

AKHQ itself is a popular open‑source web UI for Apache Kafka, but its native API is limited to HTTP endpoints that require authentication and manual polling. The MCP server wraps these endpoints into a standardized protocol, translating AI requests into AKHQ API calls and returning structured JSON responses. This abstraction lets the assistant ask natural language questions such as “Show me the top 5 lagging consumers in topic ” and receive a concise, machine‑readable reply that can be rendered as tables or charts within the assistant’s UI. The server also supports real‑time subscriptions, enabling continuous updates for monitoring dashboards.

Key capabilities include:

• Topic and consumer group introspection – Retrieve metadata, partition counts, and lag metrics on demand.
• Cluster health monitoring – Access broker status, topic replication factors, and message throughput statistics.
• Query filtering and pagination – Limit results to relevant subsets, reducing bandwidth and improving response times.
• Secure integration – Leverage existing AKHQ authentication mechanisms, ensuring that AI assistants only access authorized data.
• Extensible command interface – Developers can add custom MCP commands to expose additional AKHQ features or internal metrics.

Typical use cases span from everyday debugging—such as spotting a stalled consumer group—to proactive alerting, where an assistant can notify developers of sudden spikes in message latency. In data‑driven product teams, the server allows product managers to ask “What is the current backlog on topic ?” and receive an instant answer without switching tools. For operations teams, the MCP server can be used to generate incident reports or run automated health checks as part of a broader observability stack.

By integrating AKHQ with AI assistants through MCP, developers gain a powerful, low‑friction workflow that combines the best of human intuition and machine precision. The server’s lightweight design means it can be deployed alongside existing AKHQ installations with minimal overhead, making it an attractive addition to any Kafka‑centric development environment.