"A tool for PostgreSQL database querying and schema inspection" matching MCP servers:

Enables AI applications to securely access and manage PostgreSQL data in Azure via MCP tools for querying, updating, and schema operations.

A Model Context Protocol (MCP) Server that allows AI models to securely interact with data hosted in Azure Database for PostgreSQL. It enables natural language querying, schema exploration, and data management through MCP clients like Claude Desktop and Visual Studio Code.

MCP server for querying and managing multiple databases (SQLite, PostgreSQL, MySQL) with read-only mode and schema inspection.

A production-oriented MCP server for PostgreSQL that exposes database operations like query execution, schema introspection, and table metadata to MCP clients such as Claude Desktop and VS Code.

A read-only MCP server for PostgreSQL schemas and their git history, enabling AI agents to inspect schema details, migrations, ERDs, missing indexes, circular foreign keys, and churn without write access.

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.

MCP server for multiple databases (PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis) with tools for schema inspection, querying, performance diagnostics, and safe write operations, featuring access modes, PII masking, and audit logging.

A read-only MCP server that enables AI agents to explore database schemas and execute safe queries on PostgreSQL and MySQL.

Enables AI applications to run SQL queries and retrieve results from Oracle Database.

An MCP server that provides targeted PostgreSQL schema access to AI agents, retrieving only relevant tables for a question and enforcing read-only access through multiple layers.

Provides an AI agent with read-only PostgreSQL access, enabling schema browsing, foreign-key exploration, column search across tables, capped SELECT queries, and offline data dictionary export while rejecting write operations.

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 automated schema management for Baserow, including creating, updating, and deleting tables and fields, with built-in 2FA TOTP authentication.

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

A secure MCP server that lets AI assistants directly interact with PostgreSQL databases via tools like create_table, list_tables, and execute_sql, plus a web configurator for managing connections.

An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.

A database MCP server providing 33 tools across PostgreSQL, MySQL, and SQLite, with connection groups, rollback, dump/restore, and schema auto-discovery via natural language.

Exposes tools that let AI coding agents browse a repo's schema snapshot of tables and columns and validate their SQL against it before running or writing it, so queries referencing invented or stale table and column names get caught and corrected up front. Provides list_tables, describe_table, search_columns, and check_sql over the same snapshot used by the CLI, hook, and CI check.

A Model Context Protocol server that enables powerful PostgreSQL database management capabilities including analysis, schema management, data migration, and monitoring through natural language interactions.