Discover 127 MCPs built for Google Gemini.
Enables users to upload PDF documents and query their content using natural language through a Retrieval-Augmented Generation (RAG) system.
Orchestrates a multi-agent system to provide hierarchical LLM critique and synthesis for enhanced decision-making and idea evaluation.
Transforms meeting recordings into structured, actionable intelligence using Google Gemini AI.
Documents a 30-day learning journey focused on AI-Driven Development (AIDD) through 15 structured tasks.
Develops stateful conversational AI agents and automated content generation workflows leveraging LangChain and Google's Gemini models.
Provides a basic implementation of the Model Context Protocol (MCP) allowing client-server communication with LLMs.
Provides a Retrieval-Augmented Generation (RAG) server for efficient document ingestion, vector storage, and AI-powered question answering.
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