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    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.
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    MIT
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    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.
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    MIT
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    Enables AI agents to self-report bugs, ambiguities, and improvement requests via a single report_issue tool, logging to PostgreSQL or JSONL with severity and mode classification.
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    LGPL 3.0
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    Diagnoses and manages PostgreSQL logical replication including slot WAL retention, walsender lag, stuck subscriptions, and provides gated remediation tools for safe AI-driven operations.
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    GPL 3.0
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    DBeast MCP is a robust, security-first Model Context Protocol server designed to empower AI coding assistants (like Claude Desktop) with deep PostgreSQL database insights. Instead of just running basic SQL queries, DBeast equips your AI agent with tools to inspect active locks, audit slow queries, analyze table schemas, and safely generate index recommendations in real-time. Built specifically for
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    MIT
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    Enables AI agents to read and optionally write to PostgreSQL databases through a multi-project setup with schema inspection, query execution, and performance analysis tools.
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    Read-only MCP server for on-prem Linux VMs and PostgreSQL over SSH. Check service health, retrieve bounded logs, inspect DB state, and explore table schemas — without terminal access.
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    An MCP server for tracking and managing AI command usage history using a PostgreSQL database. It enables users to log, search, and view statistics for various AI-related commands and their execution contexts.
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    An open-source MCP server for PostgreSQL that provides database health analysis, index tuning, query plan optimization, and safe SQL execution, suitable for both development and production environments.
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    MIT
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    An MCP server for Prometheus-based monitoring that enables users to query system metrics, resource usage, and PostgreSQL health across multiple environments. It supports both predefined diagnostic checks and custom PromQL execution for comprehensive server monitoring and troubleshooting.
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    Provides AI-powered PostgreSQL performance tuning capabilities including query analysis, smart index recommendations with HypoPG hypothetical testing, and comprehensive database health monitoring.
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    Apache 2.0
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    Enables AI agents to interact with PostgreSQL databases through schema intelligence, query execution, and DBA tooling including index analysis and health monitoring. Features configurable access levels and audit logging for secure database operations.
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    1,173
    MIT
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    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.
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    MIT
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    Enables AI assistants to analyze and optimize PostgreSQL database performance by identifying missing indexes and suggesting query rewrites. It allows users to retrieve database projects and implement performance fixes directly through natural language in their code editor.
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    MIT
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    Enables interaction with NetBox infrastructure management platform to query and manage devices, VMs, IP addresses, and VLANs. Features secure HashiCorp Vault integration and PostgreSQL state tracking for comprehensive infrastructure automation.
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    2