"Information about PostgreSQL, a relational database system" matching MCP servers:

A production-grade MCP server with 6 sandboxed tools and an agent orchestration engine for autonomous task completion, featuring an evaluation suite with CI/CD quality gates.

provides AI-powered PostgreSQL performance tuning capabilities.

A LangGraph-powered MCP server for infrastructure orchestration with autonomous subagents.

Record an agent's MCP tool-call workflow once, replay it deterministically for zero tokens, and get a receipt — a snapshot-testing and deterministic-execution layer for Agent Skills.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

Analyzes database migrations for risks like lock contention and data loss before they run. Supports Flyway and Liquibase formats, providing risk scores and rollback generation.

An MCP server that scaffolds full-stack projects with consistent structure, Docker setup, CI/CD pipelines, and database configuration.

Developers are constantly feeding context files to AI tools like Cursor, Windsurf, Copilot Workspace, and Claude Code. If your agent doesn't understand your real database schema, it writes code that fails in production. LintBase acts as the bridge. It connects directly to your database, reads the ground truth of your live documents, and generates structured context optimized for AI. 🤖 Stops AI

An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.

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.

MCP server and CLI for AlmaLinux Build System (ALBS) that enables AI coding assistants to investigate build failures, create builds, sign packages, and manage release plans through natural language.

Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.

Enables retrieving comprehensive information about GitLab merge requests, including details, commits, diffs, discussions, approvals, and CI/CD status, through natural language.

A local MCP server that exposes Bazel build system capabilities to AI assistants.

MCP server for Beaker lab automation, enabling system provisioning, job management, distro discovery, and failure diagnosis through 25 tools.

LoopSense is an open-source MCP server that closes the feedback loop for AI coding agents — giving them real-time visibility into CI results, deployments, test outcomes, and file system changes.

An MCP server that enables AI assistants to manage database migrations and validate schemas using the Ariga Atlas CLI. It provides tools for applying migrations, generating diffs, linting, and checking migration status through natural language.

A local MCP server that exposes Bazel build system functionality to AI agents, allowing them to build, test, query, and manage Bazel projects through natural language even in environments where Bazel can't be directly accessed.

A multi-Jenkins server management and automation tool that enables intelligent scenario mapping, multi-server management, and complete CI/CD lifecycle operations through natural language interactions.

A demonstration MCP server built with FastMCP v2.0 that provides basic mathematical calculations and greeting functionality. Features Docker containerization, comprehensive testing, and CI/CD automation for learning MCP development patterns.