pg-analytics-mcp 0.3.0

MCP server for PostgreSQL analytics — schema discovery, data exploration, relationships, performance, data quality, multi-env support

pg-analytics-mcp

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

What it does

Exposes 26 read-only tools organised in 7 categories:

Schema Discovery

• database_summary — high-level overview: schema/table/view/FK/index counts, total size, extensions
• scan_schemas — row counts for every table, grouped by schema
• describe_table — column details: name, type, nullable, default, position
• table_sizes — disk usage (data + indexes + toast) per table, ordered by size
• 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/PK ordering)
• column_value_counts — distinct values and frequencies for a column
• column_stats — min, max, avg, null count, distinct count for a column

Relationships

• list_constraints — all constraints (PK, unique, check, FK) for a table
• foreign_keys — bidirectional FK relationships (incoming + outgoing)
• compare_envs — compare row counts across DEV / STG / PROD

Performance

• index_usage — index scan stats and unused index detection
• slow_query_candidates — tables with high sequential scan counts (missing index candidates)
• bloat_estimate — tables with dead tuples that may need VACUUM

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

Pipeline Failures (v2)

• pipeline_fail_tables — discover all pipeline.*_fails tables with row counts and stats
• pipeline_fail_summary — cross-entity failure summary grouped by entity, stage, or both
• pipeline_fail_details — drill into a specific entity's fail table with optional filters
• pipeline_fail_runs — analyse which pipeline runs generated the most failures

Legacy (pipeline-specific)

• ingestion_failures — recent records from pipeline.ingestion_failures (legacy monolithic table)
• ingestion_failures_summary — failures grouped by table + error reason (legacy monolithic table)

Quick start

Prerequisites

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

Install

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

uvx pg-analytics-mcp

Option B — clone and run:

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

Configure

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

Variable	Description	Required
PG_DEV_URL	PostgreSQL DSN for DEV	At least one URL
PG_STG_URL	PostgreSQL DSN for STG	Optional
PG_PROD_URL	PostgreSQL DSN for PROD	Optional
PG_READ_ONLY	Reserved for future write tools (not yet used)	Optional

export PG_DEV_URL="postgresql://user:pass@host:5432/dbname"
export PG_STG_URL="postgresql://user:pass@host:5432/dbname"   # optional
export PG_PROD_URL="postgresql://user:pass@host:5432/dbname"  # optional

Supports postgresql+asyncpg:// URLs (the driver prefix is stripped automatically).

Add to Claude Code

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

If using uvx:

{
  "mcpServers": {
    "pg-analytics": {
      "command": "uvx",
      "args": ["pg-analytics-mcp"],
      "env": {
        "PG_DEV_URL": "postgresql://user:pass@host:5432/dbname",
        "PG_STG_URL": "postgresql://user:pass@host:5432/dbname"
      }
    }
  }
}

If installed from clone:

{
  "mcpServers": {
    "pg-analytics": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/pg-analytics-mcp", "pg-analytics-mcp"],
      "env": {
        "PG_DEV_URL": "postgresql://user:pass@host:5432/dbname"
      }
    }
  }
}

Security

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

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

Multi-environment support

Configure up to 3 environments (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 pg_analytics_mcp/    # lint
uv run python -m pg_analytics_mcp      # start server locally

License

MIT