ClickHouse Dataops MCP

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Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/aguantar-clickhouse-dataops-mcp

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What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

6 read · 1 write · 0 delete · 1 not yet classified

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Python package

Tools (8)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• ch_data_quality

  Run data quality checks: nulls, duplicates, gaps, and market coverage. Checks for a specific date: - Null/empty values per column - Duplicate rows by primary key - Hourly data gaps (missing time windows) - Market coverage (are all 5 coins present?) - Data freshness Args: table: Table to check (default: crypto_trades) database: Database name (default: cdc_pipeline) check_date: Date to check in YYYY-MM-DD format (default: today)

• ch_disk_usage

  Analyze disk usage by table and partition with recommendations. Returns: - Total disk usage for the database - Per-table breakdown (rows, size, parts, percentage) - Per-partition breakdown (top 20 by size) - TTL information - Recommendations (excessive parts, missing TTL on large tables) Args: database: Database to analyze (default: cdc_pipeline)

• ch_explain_query

  Analyze a query's execution plan and suggest optimizations. Runs EXPLAIN PLAN and EXPLAIN PIPELINE, then provides structured analysis: - Whether partition pruning is active - Whether sorting keys are utilized - Specific optimization suggestions (add time filters, use pre-aggregated tables, etc.) This is the key differentiator from generic ClickHouse tools — it doesn't just execute queries, it advises on how to write better ones. Args: sql: The SELECT query to analyze database: Target database (default: cdc_pipeline)

• ch_list_tables

  List all tables with metadata and built-in descriptions. Returns table name, engine type, partition/sorting keys, row count, disk size, and a human-readable description of each table's purpose. Use this as the starting point to understand what data is available. Args: database: Database to list (default: cdc_pipeline)

• ch_pipeline_latency

  Analyze CDC pipeline latency by segment. Measures latency at each stage of the pipeline: - source_to_cdc: Upbit → Debezium/Kafka - cdc_to_flink: Kafka → Flink processing - flink_to_insert: Flink → ClickHouse write - end_to_end: total source_ts → inserted_at Each segment reports p50, p95, p99, and max latency in milliseconds. Also includes data freshness (seconds behind real-time) and per-market breakdown. Args: market: Filter by market (e.g., "KRW-BTC"). Empty = all markets period: Time window — "10m", "1h", "6h", or "24h" (default: "1h")

• ch_query

  Execute a read-only SQL query against ClickHouse. Runs SELECT queries with automatic safety validation (DDL/DML blocked), LIMIT enforcement, and partition pruning analysis. Returns results with a warning if the query doesn't leverage partitioning efficiently. Args: sql: SELECT query to execute database: Target database (default: cdc_pipeline) max_rows: Maximum rows to return (default: 1000, max: 10000)

• ch_slow_queries

  Find slow queries with root cause diagnosis. Scans system.query_log for queries exceeding the duration threshold, then generates a diagnosis for each: - Full scan detection (high read_rows without partition pruning) - Memory-intensive query detection - Aggregation optimization suggestions (use pre-aggregated tables) - Error detection Args: hours: Look back period in hours (default: 24) min_duration_ms: Minimum query duration to report (default: 1000ms) limit: Maximum number of slow queries to return (default: 20)

• ch_table_schema

  Get comprehensive table metadata: columns, engine, keys, partitions, and sample data. Returns column types, partition/sorting/primary keys, TTL settings, row counts, disk usage, partition breakdown, and 5 sample rows. Essential for understanding table structure before writing queries. Args: table: Table name database: Database name (default: cdc_pipeline)

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32

Last commit

Mar 19, 2026 (7 months ago)

Last release

Mar 19, 2026 (7 months ago)

Last package publish

Mar 19, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.Aguantar/clickhouse-dataops-mcp

Version

0.1.1

Auth

API key

Packages

pypi clickhouse-dataops-mcp

License

MIT

Repository

github.com/Aguantar/clickhouse-mcp-server

First seen

Oct 24, 2026

Last checked

Oct 25, 2026

Source

Official MCP Registrygithub-graphql