"Creating a Postgres MCP server in Python" matching MCP servers:

A Model Context Protocol server that provides secure database access to PostgreSQL through Kysely ORM, enabling natural language interaction with PostgreSQL databases. It offers 40+ MCP tools for querying, schema inspection, diagnostics, and Java backend monitoring.

Enables AI assistants to manage, monitor, and optimize PostgreSQL databases with over 200 specialized tools for operations, security, performance tuning, and diagnostics.

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.

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.

A Model Context Protocol (MCP) server for PostgreSQL that provides safe, structured access to your database for AI assistants, enabling health checks, index tuning, lock analysis, and more.

Read-only PostgreSQL analytics MCP server — query plans, slow queries, index usage, table bloat, vacuum status. No DDL/DML/writes. Curated by Archimedes Market with a verified Trust Report.

An MCP server for PostgreSQL with index tuning, explain plans, health checks, and safe SQL execution.

MCP server for safely exposing SQL Server database capabilities to LLM clients, with read-only mode, security features, and observability.

A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

Enables AI assistants to safely inspect and query PostgreSQL databases with read-only enforcement, row/timeout limits, and audit logging, providing tools for schema/table listing, read-only queries, query explanations, and table statistics.

An MCP server that enables AI agents to safely operate multi-cluster Kubernetes fleets through an Open Cluster Management hub, with policy enforcement, human approval, and audit tracing.

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

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

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 for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

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 managing Uptime Kuma monitoring through natural language, including creating, editing, pausing, and deleting monitors, plus retrieving heartbeats, status, metrics, notifications, and status pages.

Enables AI assistants to query Grafana datasources (Prometheus/Loki metrics and SQL databases like ClickHouse/Postgres/MySQL) and search or inspect dashboards through natural language, using read-only access to Grafana's API.

MCP server for inspecting IBM Liberty/WebSphere application servers over SSH, enabling reading sanitized configs, performing health checks, and searching logs remotely.