"A server for interacting with PostgreSQL and executing SQL scripts" matching MCP servers:

Enables Azure SQL Database performance and administration through typed execution, query benchmarking, and managed DBA operations.

A production-ready MCP server for SQL Server monitoring, diagnostics, and database operations, with native connectivity, session management, deadlock and blocking analysis, and health checks.

Enables AI assistants to perform read-only PostgreSQL DBA analysis and monitoring through natural language, covering performance, bloat, locks/deadlocks, autovacuum, and schema inspection across PostgreSQL 12-18 without required extensions.

Enables AI assistants to perform read-only PostgreSQL DBA work across versions 12–18, including performance analysis, slow query identification, bloat detection, lock and deadlock monitoring, autovacuum and schema inspection, and replication/WAL checks via natural language. Works with regular user permissions on RDS/Aurora without requiring any extensions, while optionally leveraging pg_stat_statements and pg_stat_monitor for deeper query analytics.

Enables AI assistants to monitor and administer PostgreSQL 12-18 through natural language, covering performance and slow-query analysis, table bloat detection, lock and connection monitoring, replication and WAL status, and VACUUM/autovacuum and schema inspection. It works read-only out of the box with automatic version detection and optional extension-enhanced query analytics.

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 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 perform PostgreSQL database operations and monitoring through natural language, including performance analysis, bloat detection, lock and deadlock monitoring, autovacuum and schema inspection across PostgreSQL 12-18.

Enables AI assistants to inspect and monitor PostgreSQL 12-18 databases through natural language, covering performance analysis, slow-query identification, bloat and lock detection, autovacuum health, replication/WAL status, and schema exploration. All operations are read-only and work without requiring any extensions, though pg_stat_statements and pg_stat_monitor optionally enhance query analytics.

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

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 monitor and administer PostgreSQL 12-18 databases through natural language, covering performance and slow-query analysis, table bloat detection, connection and lock monitoring, autovacuum and maintenance recommendations, and schema discovery. All operations are read-only and work with regular user permissions, requiring no extensions unless optional query-analytics features are desired.

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, with no extensions required.

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 natural-language PostgreSQL operations and monitoring across PG 12-18 without requiring extensions, covering performance analysis, slow-query identification, bloat detection, lock and connection monitoring, autovacuum inspection, replication/WAL status, and schema relationship discovery. All operations are read-only and production-safe, working with regular user permissions on RDS/Aurora as well as self-hosted instances.

Enables AI assistants to perform PostgreSQL operations and monitoring through natural language, including performance analysis, bloat detection, lock/deadlock monitoring, autovacuum inspection, schema discovery, and maintenance recommendations with production-safe read-only tools across PostgreSQL 12–18.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables secure, AI-driven PostgreSQL database administration, observability, and querying with support for extensions like PostGIS and pgvector, connection pooling, and advanced tool filtering.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Enables AI-driven SOC investigations by providing automated Splunk querying, threat intelligence enrichment, and response actions through natural language. Includes tools for IP pivoting, lateral movement detection, and label harvesting.

A Model Context Protocol server that provides seamless integration with Coroot observability platform, enabling monitoring of applications, analysis of performance metrics, examination of logs and traces, and management of infrastructure through Coroot's comprehensive API.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.