Kafka Dataops Mcp
Diagnose Kafka consumer lag and broker health from your agent when event-driven features misbehave in production.
Overview
io.github.Aguantar/kafka-dataops-mcp is a MCP server for the Operate phase that helps agents diagnose Kafka consumer lag and monitor brokers.
What is this MCP server?
- Stdio MCP server (PyPI kafka-dataops-mcp, v0.1.0) for agent-driven Kafka ops
- Configured with KAFKA_BOOTSTRAP_SERVERS for cluster access
- Consumer lag diagnosis for stuck or falling-behind consumers
- Broker monitoring oriented to DataOps troubleshooting
- GitHub source: Aguantar/kafka-mcp-server
- Server version 0.1.0
- Transport: stdio
- PyPI identifier: kafka-dataops-mcp
What problem does it solve?
Kafka outages feel opaque when you cannot quickly see lag and broker state from the same place you are already fixing code with an AI agent.
Who is it for?
Small teams operating Kafka for async jobs or streaming who want MCP-native incident context beside their codebase.
Skip if: Greenfield projects not using Kafka, or organizations that require only vendor-managed streaming with no self-hosted brokers.
What do I get? / Deliverables
With bootstrap servers configured, your agent can surface lag and broker context so you can scale consumers or fix consumers faster.
- MCP tools for lag and broker-oriented Kafka checks
- Operational context you can pair with code changes or consumer config updates
- Stdio integration documented in server.json v0.1.0
Recommended MCP Servers
Journey fit
Consumer lag and broker alerts are classic post-launch production problems for teams running Kafka-backed queues or streams. Monitoring matches lag diagnosis, broker metrics, and live cluster checks rather than initial topic design in Build.
How it compares
Kafka monitoring MCP bridge, not a topic-design skill or managed Kafka provisioning tool.
Common Questions / FAQ
Who is kafka-dataops-mcp for?
Builders shipping event-driven SaaS or backends on self-managed or reachable Kafka who want agent-assisted lag and broker checks.
When should I use kafka-dataops-mcp?
Use it in Operate when consumers fall behind, partitions rebalance oddly, or you suspect broker issues during an incident.
How do I add kafka-dataops-mcp to my agent?
Install kafka-dataops-mcp from PyPI, set KAFKA_BOOTSTRAP_SERVERS to your cluster, and add the stdio MCP server entry in your agent configuration.