DataOps Kafka MCP server with consumer lag diagnosis and broker monitoring
Kafka MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. DataOps Kafka MCP server with consumer lag diagnosis and broker monitoring.
A DataOps-focused Kafka MCP server with consumer lag diagnosis and broker health monitoring. Diagnosis logic is based on actual CDC pipeline operational experience.
You will need one environment variable: KAFKA_BOOTSTRAP_SERVERS. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
The server ships on PyPI as kafka-dataops-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
Plenty of monitoring and observability servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by Aguantar; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"kafka-dataops": {
"command": "uvx",
"args": ["kafka-dataops-mcp"],
"env": {
"KAFKA_BOOTSTRAP_SERVERS": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| KAFKA_BOOTSTRAP_SERVERS | Configuration value read at startup. | Optional |
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