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Integrates weather API services with multiple AI models, demonstrating a complete Model Context Protocol (MCP) client-server architecture.
Provides AI assistants with tools to load, parse, and query OpenAPI and Swagger API documentation.
Scrapes data from the Longman Dictionary website and returns it in a standardized JSON format.
Illustrates fundamental multi-agent system concepts, hierarchical agent architectures, and AI agent tool execution using the Google Agent Development Kit (ADK).
Provides comprehensive web research and discovery capabilities through 13 specialized tools for searching, crawling, and analyzing web content.
Provides efficient search and referencing capabilities for user-configured documentation.
Equips AI assistants with structured external memory, eliminating context loss and enabling an experienced teammate role in software development.
Extracts video metadata and captions from YouTube videos, converting them to markdown with customizable templates.
Generates educational explanation videos based on topics using the Scenext AI video generation platform.
Enables access to the YouTube Translate API for obtaining transcripts, translations, and summaries of YouTube videos.
Enables Claude to access and search documentation from LangChain, LlamaIndex, and OpenAI.
Provides AI assistants with instant, accurate Maven Central dependency intelligence and documentation support for all JVM build tools.
Enables access and searching of Lark (Feishu) documents via a Model Context Protocol server.
Enables AI assistants to perform full-text searches and retrieve content from Obsidian vaults using the Model Context Protocol.
Creates an MCP server that manages a simple note-taking system using a low-level server with streamable HTTP.
Provides offline Motion.dev documentation and intelligent animation code generation for React, JavaScript, and Vue projects.
Demonstrates the Model Context Protocol (MCP) for AI assistants to interact with external tools and data sources.
Manages and semantically searches a personal library of AI prompts using RAG-powered semantic search, accessible via the Model Context Protocol.
Enables AI agents to search documentation within HexDocs using the search API.
Exposes Go project insights and static analysis to large language models for enhanced understanding and code generation.
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