I recently helped debug a 600+ line Kusto query powering a production feature (years of data, many joins, summaries).
My usual method is to break the query down and measure total CPU + scanned data for each step, but doing this manually quickly becomes unmanageable.
So I extended my Kusto MCP server to return usage stats, and wrote an MCP prompt that guides the analysis automatically — breaking down queries, collecting metrics, and outputting a performance report with bottlenecks + recommendations.
This led to finding the real issue (lots of tiny fragments of data building up daily), and allowed us to suggest merge policies and scoped joins that cut CPU and scanned data dramatically.