"How to perform SELECT queries in PostgreSQL" matching MCP servers:

A general-purpose PostgreSQL MCP server with full read-write SQL access, atomic multi-statement transactions, and schema inspection. Works with any PostgreSQL instance — local, Supabase, AWS RDS, or self-hosted — and connects to Claude, Cursor, Windsurf, or any MCP-compatible AI client.

Full-featured MCP server that exposes 36 tools for interacting with PostgreSQL databases, covering schema introspection, query execution, data exploration, performance monitoring, security auditing, and maintenance.

Enables comprehensive PostgreSQL database monitoring, analysis, and management through natural language queries. Provides performance insights, bloat analysis, vacuum monitoring, and intelligent maintenance recommendations across PostgreSQL versions 12-17.

A Model Context Protocol server that enables interaction with PostgreSQL databases for analyzing setups, debugging issues, managing schemas, migrating data, and monitoring performance.

Provides long-term memory for AI agents via MCP tools to store, recall, and delete memories, with per-user scoping and usage limits.

Persistent memory and pattern learning for AI coding assistants via MCP.

A postgresql mcp that you can run your DQL SQL though it

Enables AI assistants to monitor and operate PostgreSQL databases through natural language queries, covering performance analysis, bloat detection, lock/deadlock monitoring, autovacuum and schema inspection, and maintenance recommendations with read-only, extension-optional workflows across PostgreSQL 12–18.

Enables AI assistants to perform PostgreSQL database operations, monitoring, and management via natural language, covering performance analysis, bloat detection, lock/deadlock monitoring, autovacuum and schema inspection with read-only production-safe queries.

Enables AI assistants to perform read-only PostgreSQL DBA and monitoring tasks, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum and schema inspection across PostgreSQL 12–18. It supports natural-language queries against production-safe, RDS/Aurora-compatible connections, with optional query analytics via pg_stat_statements or pg_stat_monitor.

Enables AI assistants to perform PostgreSQL database operations and monitoring through natural language, including performance analysis, bloat detection, lock and connection monitoring, autovacuum intelligence, and schema/relationship discovery. Works read-only across PostgreSQL 12-18 with no extensions required, with optional pg_stat_statements and pg_stat_monitor support for advanced query analytics.

Enables natural-language monitoring and administration of PostgreSQL databases, covering performance analysis, bloat detection, lock and deadlock monitoring, autovacuum intelligence, and schema inspection. It works with PostgreSQL 12-18 and can run without extensions, with optional extensions for advanced query analytics.

Enables AI assistants to inspect and monitor PostgreSQL databases with read-only DBA tools for performance analysis, bloat detection, locks/deadlocks, autovacuum, schema discovery, replication, and maintenance recommendations.

Enables AI assistants to inspect and monitor PostgreSQL databases using natural language, covering performance analysis, bloat detection, lock/deadlock monitoring, autovacuum, schema inspection, and maintenance recommendations across PostgreSQL 12–18 without requiring extensions.

Enables AI assistants to inspect and monitor PostgreSQL 12–18 databases through natural language, covering performance and slow-query analysis, table bloat detection, connection/lock and deadlock monitoring, autovacuum and VACUUM effectiveness, replication/WAL status, and schema and relationship discovery. All operations are read-only and production-safe, working with regular user permissions on RDS/Aurora, with optional pg_stat_statements and pg_stat_monitor extensions for deeper query analytics.

Enables AI assistants to perform PostgreSQL DBA operations and monitoring through natural language, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum and schema inspection across PostgreSQL 12–18.

Enables AI assistants to perform PostgreSQL database operations, monitoring, and management through natural language, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum, and schema inspection. Supports PostgreSQL 12-18 with read-only production-safe tools and optional extension-enhanced query analytics.

Enables AI assistants to perform PostgreSQL database operations, monitoring, and management through natural language, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum and schema inspection, and maintenance recommendations across PostgreSQL 12-18.

Enables Claude Desktop to interact with PostgreSQL databases through natural language for schema exploration, data analysis, and query execution. Users can search schemas, describe tables, and perform read or write operations without needing to write manual SQL.

Enables AI assistants to perform read-only PostgreSQL DBA operations through natural language, including performance and slow-query analysis, bloat and capacity detection, lock/deadlock and connection monitoring, autovacuum/VACUUM effectiveness checks, and schema and relationship discovery. It works across PostgreSQL 12-18 with automatic version detection and no required extensions, optionally enhancing query analytics via pg_stat_statements or pg_stat_monitor.