"Connecting to a PostgreSQL database on Amazon RDS" matching MCP servers:

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

A secure MCP server that lets AI assistants directly interact with PostgreSQL databases via tools like create_table, list_tables, and execute_sql, plus a web configurator for managing connections.

A server that provides management and connectivity for Alibaba Cloud RDS database services via OpenAPI, enabling users to create, query, and modify RDS instances through MCP integration.

Enables AI assistants to query Amazon search volume trends and growth signals for product research and consumer purchase intent analysis.

placement for the depractaed Amazon Product Advertising endpoint. You ask Claude to find something, it searches the live Amazon catalogue, and it hands you back a finished HTML card - image, price, savings, star rating, your Associates tag already baked into the link, and the disclosure footer Amazon requires, by Houtini

Agent-native Amazon review intelligence — fetches verified reviews from 10 marketplaces via real Shulex OpenAPI (not scrapers) and produces copy-ready listing improvements grounded in actual customer language. Backed by a 2B-review historical dataset that Helium 10 / Jungle Scout can't replicate. Works in any MCP client (Claude Code, Claude Desktop, ChatGPT, Cursor, Windsurf, VS Code, Cline).

Connects Claude to your Amazon Seller Central account via the Selling Partner API, enabling queries for recent orders, sales summaries, FBA inventory, and financial events.

MCP server for querying and managing multiple databases (SQLite, PostgreSQL, MySQL) with read-only mode and schema inspection.

A small MCP server that lets an LLM query PostgreSQL, MySQL, MariaDB, SQL Server, or SQLite databases safely — read-only, role-restricted, and with sensitive data blacked out.

A database MCP server providing 33 tools across PostgreSQL, MySQL, and SQLite, with connection groups, rollback, dump/restore, and schema auto-discovery via natural language.

Enables AI assistants to connect to and interact with PostgreSQL, MySQL, SQLite, and MongoDB databases through natural language, supporting schema exploration, query execution, data export, and more.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

A high-performance Model Context Protocol (MCP) server that provides seamless integration with Amazon DataZone services. This server enables AI assistants and applications to interact with Amazon DataZone APIs through a standardized interface.

An MCP server that enables AI assistants to interact with Amazon Connect contact centers, providing 88 tools, 91 configuration templates, and multi-region support.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Enables creating and editing Amazon listing images and product videos from live catalog data, with Amazon compliance rules, character-consistent generation, and resumable video generation.

Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.

Enables intelligent navigation and extraction of documentation from websites, allowing Amazon Q to automatically discover relevant pages, extract clean content, and retrieve code examples from web documentation.

Enables agents to retrieve indexed Amazon Ads, Seller Central, and Vendor Central knowledge by browsing tags, searching tools, inspecting schemas, and executing chained calls.