"postgres" matching MCP servers:

provides AI-powered PostgreSQL performance tuning capabilities.

Blocks unsafe PostgreSQL migrations before an AI agent writes or runs them. check_before_apply returns a pass/fail gate; reads the real Postgres parser, classifies the lock each statement takes, checks 112 safety rules. Runs offline, no database required.

Zero-config data quality monitoring as MCP tools. Profiles a warehouse (Postgres, BigQuery, Snowflake, MySQL, DuckDB), detects anomalies, and gates CI — read-only with the connection resolved server-side, never via the model.

Agentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.

Enables coding agents to deploy Next.js apps and provision Postgres, Redis, MinIO, NATS, Mailpit, and nginx locally via Docker without requiring Vercel or Supabase signups, using a single account and reading logs from the same environment.