"How to Control Apple Music" matching MCP servers:

Provides access to Apple documentation and WWDC transcripts with semantic, keyword, and hybrid search capabilities, enabling developers to quickly find relevant code examples and technical information.

A Model Context Protocol server that enables semantic search and RAG over your Apple Notes, allowing AI assistants like Claude to search and reference your notes during conversations.

Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.

Provides AI agents with instant access to official Apple developer documentation, Swift programming guides, design guidelines, and Apple Developer YouTube content including WWDC sessions. Uses advanced RAG technology with semantic search and AI reranking to deliver accurate, contextual answers for Apple platform development.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables AI assistants to search and edit Apple Notes on macOS using semantic search, with safe write operations and full local indexing.

Enables managing and controlling AnythingLLM instances through MCP, providing tools to list, create, and delete workspaces, retrieve system environment settings, and get vector counts.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables AI clients to search curated, cited knowledge bases built from founders', CEOs', and investors' public talks, essays, and posts, returning passages with source links and dates. It also exposes available source collections via MCP tools so users can ask how those individuals might approach business, product, or growth questions.

Enables AI coding agents to store and search team and personal know-how as small entries across local SQLite and hosted Postgres stores, with federated search and writes to the chosen store so new sessions build on prior knowledge.

Enables MCP clients to search encrypted, self-hosted archives and retrieve bounded records from Apple, Canvas, Health, travel, and Obsidian sources while checking sync freshness.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.