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Optimize ClickHouse queries, analyze pipeline latency, and monitor data quality safely.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "io.github.Aguantar/clickhouse-dataops-mcp" yet — see the docs or source repo.
Analyze the performance bottlenecks of this ClickHouse SQL and provide actionable optimization advice, including indexes, partitioning, table design, or query rewrites. SQL: SELECT user_id, count() FROM events WHERE event_date >= today() - 7 GROUP BY user_id ORDER BY count() DESC LIMIT 100;
Returns a root-cause analysis of the slow query with ClickHouse-specific optimization recommendations.
Check the latency behavior of this data pipeline in ClickHouse over the last 24 hours. Identify stages with increased latency, possible causes, and the highest-priority areas to investigate.
Outputs latency trends, anomaly localization by stage, and prioritized troubleshooting recommendations.
Evaluate data quality for the orders table over the last 7 days, focusing on nulls, duplicate records, abnormal distributions, and time gaps, then summarize potential risks.
Generates data quality findings, listing detected issues, risk explanations, and follow-up monitoring suggestions.
Query ClickHouse clusters and list databases or tables via natural language.
Query massive US equity quant data for read-only cross-sectional analysis.
Query ClickHouse databases in natural language with per-call access controls.
Safely explore and analyze data across multiple databases with read-only access.
Connect to DataHub catalogs to find datasets, trace lineage, and read metadata.
Query, manage, and inspect ClickHouse databases directly through an AI assistant.