"PostgreSQL MCP (Multi-Version Concurrency Control or specific usage)" matching MCP servers:

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

A Model Context Protocol server that provides read-only access to PostgreSQL databases with enhanced multi-schema support, allowing LLMs to inspect database schemas across multiple namespaces and execute read-only queries while maintaining schema isolation.

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.

Enables AI coding agents to retrieve the latest stable versions of packages and tools across multiple ecosystems, preventing outdated dependency versions in generated code.

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.

A flexible proxy server that aggregates multiple backend MCP servers into a single interface using STDIO or SSE transports. It supports dynamic server management via an HTTP API and utilizes namespacing to prevent tool conflicts across connected services.

Suggests the latest stable package versions when writing code.

Enables LLMs to orchestrate AI agents and tasks, inspect DAG workflows, monitor cluster metrics, and perform safe database queries and statements through standardized MCP tools.

Enables multiple AI agents using MCP-compatible clients to collaborate in parallel on the same project across one or more machines, with direct messaging, a shared task board, atomic file/folder locking, presence tracking, and a live web dashboard.

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

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 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.