Enables seamless interaction with Google's Gemini 2.5 Pro Experimental AI model via full Model Context Protocol support, featuring secure API key handling, Google Search integration, token usage reporting, and TypeScript implementation.

Provides a robust Model Context Protocol (MCP) implementation for seamless, asynchronous CRUD operations and real-time updates with MongoDB, featuring secure connections, comprehensive error handling, Docker support, and SSE integration.

Natural language interface for agentic applications enabling seamless Model Context Protocol (MCP) client integration to efficiently manage, search, and manipulate structured and unstructured data in Redis with support for hashes, lists, sets, streams, vector search, and real-time pub/sub.

A powerful backend service supporting file management, MongoDB integration, external API calls, and vector database operations, designed for seamless Model Context Protocol (MCP) integration with large language models and featuring full Docker deployment and client examples.

A Python-based Model Context Protocol (MCP) server template enabling seamless integration with applications like Claude Desktop and Cursor, featuring easy setup with uv for virtual environment management and stdio communication.