"AWS Agent for Interacting with Multiple AWS Services" matching MCP servers:

A Firecrawl MCP server with built-in support for multiple API key rotation, enabling web search, scraping, crawling, extraction, and more via MCP clients.

MCP server for WeChat Mini Program debugging and automation, enabling agents to perform UI operations, screenshots, and regression testing through natural language commands.

Enables AI agents to submit software tools to the Altimateguide directory for editorial review through a single submit_tool operation. Submissions are queued as pending and never published automatically.

Read-only MCP server for discovering and ranking on-chain agents on Stellar mainnet via natural language search, reputation probes, and profile lookups.

A Model Context Protocol server for AI agents to manage tasks and track progress across projects, with features like project isolation, search, and reporting.

An MCP server for interacting with Reddit, enabling browsing, searching, content creation, and management with built-in safe mode and bot disclosure features.

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

Provides AI coding assistants with access to AccelByte Gaming Services (AGS) Extend SDK functions and models through searchable symbols and detailed documentation. Helps generate SDK code by exposing context about available functions, models, and templates for creating Extend app projects across multiple languages (Go, C#, Java, Python).

This is an MCP (Model Context Protocol) server that provides tools for interacting with the NOAA Tides and Currents API.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

Trade, analyze, and automate Polymarket prediction markets via AI. 34 tools for direct trading, smart money flow, copy trading, backtest, and portfolio management.

A Model Context Protocol server for interacting with MongoDB Databases and MongoDB Atlas. It enables AI assistants to query, manage, and operate on MongoDB data and Atlas resources through natural language.

A Model Context Protocol server that provides persistent storage, categorization, and retrieval of bookmarks through AI clients like Claude. It allows users to manage web links using custom categories and includes options for local JSON storage or AWS S3 synchronization.

A flexible proxy server that aggregates multiple backend MCP servers into a single interface using STDIO or SSE transports. It supports dynamic server management via an HTTP API and utilizes namespacing to prevent tool conflicts across connected services.

Provides tools and context for interacting with Google Cloud services, enabling natural language management of billing, monitoring, logging, IAM, Spanner, and more.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

Deterministic encode, decode, hash and convert for agents, on the same pipeline engine as the String Utility Belt web app, CLI and editor extensions: a pipeline built and checked in the browser runs unchanged from its share link. Base64/hex/URL, JWT, hashes, JSON/YAML/CSV/XML, dates and regex; tools to search, describe and run utilities or whole pipelines, and to detect input formats. Runs locally

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.