"PostgreSQL" matching MCP servers:

A production-grade Model Context Protocol server for PostgreSQL. Lets AI agents safely inspect, query, operate, and tune a Postgres database — over 100 tools spanning catalog introspection, query intelligence, natural-language SQL, structural diffs, hybrid search, graph queries, data movement, live ops, and more.

Enables AI assistants to interact with PostgreSQL databases through natural language, with schema-aware query generation and enterprise safety controls.

A drop-in Model Context Protocol (MCP) server that lets an AI agent query PostgreSQL — read-only, enforced at the database level, with real SQL validation, timeouts, cost limits, OAuth 2.1, and an audit trail.

Read-only SQL across PostgreSQL, MySQL, S3-compatible buckets and folders of Parquet, CSV and JSON files — and one query can join across all of them, federated in-process by DuckDB. Sources are given as connection strings at startup, and no tool writes: they are absent rather than disabled.

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.

Governed PostgreSQL DBA operations — slow-query, bloat, and blocking-lock RCA, index management, vacuum/analyze, and replication lag, with unbypassable audit logging (MCP + CLI), budget/runaway guards, dry-run, and undo/rollback.

Query CVEs, yanked releases, known exploits, and upgrade paths for any PostgreSQL version directly from your AI assistant.

Enables MCP-capable agents to operate PostgreSQL and PgBouncer clusters on Kubernetes—covering discovery, pooling, tuning, backups/PITR, DR, and lifecycle—with safe-by-default read-only mode and granular opt-in security controls.

Connect any AI to PostgreSQL, MySQL, or SQL Server — 24 dialect-aware tools for query, schema, optimization, and more.

A read-only PostgreSQL MCP server for AI coding agents that exposes database schema and sample data as tools, with LLM-powered semantic search enriched by a user-authored semantic layer.

MCP server for Sqemo, a web-based ERD tool with team naming standards. 34 tools: read/edit entities, attributes, and relationships; generate physical names from a shared glossary and naming rules; SQL (7 dialects) and DBML import/export; ERD lint, diff, and ALTER generation.

Enables AI assistants to interact with multiple SQL databases (PostgreSQL, MySQL, SQL Server, SQLite, Trino) through MCP, reusing existing DBeaver connections for queries, schema management, and transactions, with SSH tunneling and read-only mode support.

MCP server for Seedfast, which fills PostgreSQL databases with realistic, relationally valid test data generated from live schema, providing tools for planning, running, and inspecting seeding operations.

Connect AI agents to SQL databases (SQLite, PostgreSQL, MySQL) with a unified interface for querying data, exploring schemas, inserting rows, and exporting results to CSV. Includes safety features like dangerous query blocking and write guards.

Enables reading Tesla vehicle data from a TeslaMate PostgreSQL database, including status, trips, charging, efficiency, weather integration, and more, via 38 tools for MCP-compatible clients like Claude Code.

Enables AI agents to create and manage BasicDeploy containers with PostgreSQL, S3 storage, and public URLs, including deploying apps, running commands, viewing logs, and sharing containers.

A hybrid long-term memory server for AI assistants that stores and retrieves memories, user facts, chat history, tasks, and reminders using PostgreSQL and ChromaDB with semantic search.

Enables interaction with 20+ databases (PostgreSQL, MongoDB, Neo4j, Elasticsearch, Redis, and more) through a single unified interface, allowing cross-database queries and operations via natural language.