ch-analytics-mcp

An MCP server for ClickHouse analytics — a general-purpose "DBA-lite" toolkit that gives any MCP client (Claude Code, Cursor, etc.) instant visibility into schema structure, data quality, storage engines, performance, multi-environment comparison, and data modeling.

What it does

Exposes 30 read-only tools organised in 6 categories:

Schema Discovery

• database_summary — high-level overview: database/table/view counts, engine distribution, compression stats
• scan_databases — row counts for every table (instant from metadata, no COUNT(*))
• describe_table — column details: name, type, nullable, default, compression codec, comment
• table_sizes — disk usage (compressed + uncompressed) per table with compression ratio
• find_tables — search tables by name pattern (ILIKE)
• find_columns — find tables that have a column matching a pattern
• list_empty_tables — quickly find tables with 0 rows
• list_environments — list configured environments

Data Exploration

• recent_rows — peek at the most recent rows (auto-detects timestamp or sorting key)
• column_value_counts — distinct values and frequencies for a column
• column_stats — min, max, avg, null count, distinct count for a column

Storage & Engines

• table_engines — list tables by engine (MergeTree, Distributed, etc.) with sorting/partition/primary keys
• partition_info — partition details: part count, rows, compressed/uncompressed size, date range
• compare_envs — compare row counts across DEV / STG / PROD

Performance

• data_skipping_indices — list data skipping indices (minmax, set, bloom_filter, etc.)
• slow_query_candidates — find recent slow queries from system.query_log
• parts_analysis — analyse part counts and compression (merge pressure detection)

Data Quality

• table_health — row count + last inserted_at/updated_at for a table
• null_report — null percentage for every column in a table
• duplicate_check — find duplicate rows based on a set of columns

Data Modeling & Discovery

• infer_relationships — scan *_id columns and infer foreign-key relationships by matching table names
• column_overlap — find column names appearing in multiple tables (join keys, shared dimensions)
• data_profile — one-shot full profile per column: type, cardinality, null%, uniqueness, min/max, samples
• sample_values — distinct values with frequency, data type, avg length, and uniqueness flag
• cardinality_check — determine relationship type (1:1, 1:N, N:1, N:M) between two tables via a join column
• key_candidates — identify potential primary/natural key columns based on uniqueness and null rate
• naming_conventions — analyze column naming patterns (suffixes like _id, _at, _type, etc.)
• type_census — distribution of base data types across all visible tables
• temporal_columns — find all Date/DateTime columns with their time ranges
• table_activity — show table activity based on part modification times (active vs stale)

Quick start

Prerequisites

• Python 3.12+
• uv (recommended) or pip
• One or more ClickHouse instances

Install

Option A — run directly with uvx (no clone needed):

uvx ch-analytics-mcp

Option B — clone and run:

git clone https://github.com/fabdendev/ch-analytics-mcp.git
cd ch-analytics-mcp
uv sync

Configure

Set environment variables for each ClickHouse environment (at least one is required):

Variable	Description	Required
CH_LOCAL_URL	ClickHouse URL for LOCAL	At least one URL
CH_DEV_URL	ClickHouse URL for DEV	At least one URL
CH_STG_URL	ClickHouse URL for STG	Optional
CH_PROD_URL	ClickHouse URL for PROD	Optional
CH_INCLUDE_DATABASES	Comma-separated allowlist of databases to scan	Optional
CH_IGNORE_DATABASES	Comma-separated databases to skip (added to internal exclusions)	Optional
CH_READ_ONLY	Reserved for future write tools (not yet used)	Optional

export CH_DEV_URL="clickhouse://user:pass@host:8123/default"
export CH_STG_URL="clickhouse://user:pass@host:8123/default"   # optional
export CH_PROD_URL="clickhouse://user:pass@host:8123/default"  # optional

# Database filtering (optional — pick one, not both)
export CH_INCLUDE_DATABASES="core,trading,pipeline"  # only scan these
export CH_IGNORE_DATABASES="tmp,scratch"              # skip these

URL format: clickhouse://user:pass@host:port/database (HTTP interface, port 8123). For HTTPS: https://user:pass@host:port/database (port 8443).

If both CH_INCLUDE_DATABASES and CH_IGNORE_DATABASES are set, the include list takes precedence.

Add to Claude Code

Add to your Claude Code MCP settings (~/.claude/settings.json or .mcp.json):

If using uvx:

{
  "mcpServers": {
    "ch-analytics": {
      "command": "uvx",
      "args": ["ch-analytics-mcp"],
      "env": {
        "CH_DEV_URL": "clickhouse://user:pass@host:8123/default",
        "CH_STG_URL": "clickhouse://user:pass@host:8123/default"
      }
    }
  }
}

If installed from clone:

{
  "mcpServers": {
    "ch-analytics": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/ch-analytics-mcp", "ch-analytics-mcp"],
      "env": {
        "CH_DEV_URL": "clickhouse://user:pass@host:8123/default"
      }
    }
  }
}

Security

All tools are read-only. No data is ever modified. Additional safeguards:

• Identifier validation — all user-provided database/table/column names are validated against ^[a-zA-Z_][a-zA-Z0-9_]*$ and backtick-quoted
• Row limits — row-level queries capped at 100, aggregation queries at 200
• Query timeout — potentially expensive queries (null_report, column_stats, duplicate_check, column_value_counts, data_profile, sample_values, cardinality_check, key_candidates, temporal_columns) use a 30s max_execution_time
• Direction validation — order_dir restricted to ASC/DESC only

Multi-environment support

Configure up to 4 environments (LOCAL, DEV, STG, PROD). The first configured environment becomes the default. Use compare_envs to quickly spot row count differences across environments.

Development

uv sync --extra dev
uv run ruff check ch_analytics_mcp/    # lint
uv run python -m ch_analytics_mcp      # start server locally

License

MIT