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Retrieves relevant sections from PDF documents for use within Zed's AI Assistant.
Extracts video metadata and captions from YouTube videos, converting them to markdown with customizable templates.
Provides a server for Claude Code to efficiently query all Cangjie programming language documentation directly within the IDE.
Provides programmatic access to Pydantic-AI documentation via a Model Context Protocol (MCP) server.
Manages and semantically searches a personal library of AI prompts using RAG-powered semantic search, accessible via the Model Context Protocol.
Enhances Claude's web search capabilities using Perplexity's API, offering intelligent model selection, domain filtering, and recency control.
Demonstrates a basic JSON-RPC client and server implementation using Node.js.
Enhances development proficiency by providing best practices and guidelines for the CURSOR IDE and various programming languages.
Accesses and searches OpenTelemetry documentation using the Model Context Protocol.
Provides offline Motion.dev documentation and intelligent animation code generation for React, JavaScript, and Vue projects.
Integrates BookStack with AI systems like LibreChat, providing programmatic access to search, manage, and interact with documentation content.
Enables access and searching of Lark (Feishu) documents via a Model Context Protocol server.
Enables Claude to access and search documentation from LangChain, LlamaIndex, and OpenAI.
Fetches and summarizes developer documentation from MDN Web Docs.
Illustrates fundamental multi-agent system concepts, hierarchical agent architectures, and AI agent tool execution using the Google Agent Development Kit (ADK).
Guides large language models in applying modern design principles and best practices for web page generation.
Generates natural, fluent Chinese content using advanced language models, capable of internet searches and direct file saving.
Connects local Obsidian vaults stored in iCloud to AI via the Model Context Protocol (MCP).
Provides a command-line interface for semantic search within PyTorch documentation, leveraging embeddings and a vector database.
Guides web accessibility learners to relevant W3C resources, focusing on official documentation rather than AI-generated explanations.
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