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Queries JFK documents using the Archives API.
Discovers events, venues, and attractions through the Ticketmaster Discovery API.
Enables real-time push notifications through a Model-Context-Protocol server.
Explores the capabilities of Large Language Models through various experiments using ChatGPT and French IGN APIs.
Provides a simple example of using the FastMCP framework to develop a Shunfeng Express (SF Express) logistics query service.
Provides a Retrieval-Augmented Generation (RAG) server for efficient document ingestion, vector storage, and AI-powered question answering.
Provides a Multi-Agent Conversation Protocol (MCP) server for interacting with the Google Cloud Video Intelligence API.
Facilitates deployment and integration of AI Model Context Protocol servers with Microsoft Copilot Studio to enhance AI model capabilities with external data sources.
Enables programmatic management of Microsoft Dataverse schemas, including tables, columns, relationships, and option sets.
Enables AI assistants to analyze and read GitHub repositories, facilitating code analysis, repository exploration, and understanding project structures.
Manages a todo list with support for both standard I/O (stdio) and HTTP transports via the Model Context Protocol.
Provides a standardized bridge between AI models and the LunaTask API for task management.
Provides a read-only Model Context Protocol (MCP) interface for AI assistants to search, retrieve, recommend, and explore movie and TV show data from The Movie Database (TMDB) API.
Integrates web search capabilities into an MCP server using the Tavily API for AI agent functionality.
Provides Model Context Protocol (MCP) tools to query timetables (EDT) from the University of Caen (Unicaen).
Provides AI-powered natural language search and query capabilities for user manuals and documents.
Transforms existing backend APIs into Model Context Protocol endpoints for seamless integration with AI agents.
Builds a conversational AI virtual agent capable of answering movie-related questions and providing weather information through a multi-agent LangGraph workflow.
Perform Retrieval Augmented Generation (RAG) on local codebases using a raw Python implementation with support for multiple AI providers.
Establishes a social coding platform where AI agents can share, collaborate on, and improve code snippets.
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