"Using PostgreSQL to Execute Queries" matching MCP servers:

A general-purpose PostgreSQL MCP server with full read-write SQL access, atomic multi-statement transactions, and schema inspection. Works with any PostgreSQL instance — local, Supabase, AWS RDS, or self-hosted — and connects to Claude, Cursor, Windsurf, or any MCP-compatible AI client.

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.

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.

A Model Context Protocol server that enables interaction with PostgreSQL databases for analyzing setups, debugging issues, managing schemas, migrating data, and monitoring performance.

A postgresql mcp that you can run your DQL SQL though it

Enables Claude Desktop to interact with PostgreSQL databases through natural language for schema exploration, data analysis, and query execution. Users can search schemas, describe tables, and perform read or write operations without needing to write manual SQL.

A Model Context Protocol server that converts Markdown documents to plain text with flexible options and dual protocol support.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Auto-generates MCP tools from your OpenAPI spec, allowing natural language interaction with any API via configurable headers and serverless deployment.

A minimal MCP server that auto-generates tools from your OpenAPI spec, optimized for Vercel.

A standalone proxy that transforms any OpenAPI or Swagger-described REST API into an MCP server by mapping API operations to executable MCP tools. It enables AI clients to interact with existing web services through automated HTTP requests based on their official documentation.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Automatically converts Swagger/OpenAPI specifications into dynamic MCP tools, enabling interaction with any REST API through natural language by loading specs from local files or URLs.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Converts natural language queries into valid GraphQL queries and executes them against GraphQL APIs. Includes schema introspection, query validation, execution with authentication, and query history tracking.

An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Enables two LLMs to play Tic-Tac-Toe against each other autonomously using a shared tool and an SSE relay. The server facilitates agent-to-agent communication by holding tool responses until the opponent makes a move, managing the game state in real-time.