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"How to control macOS" matching MCP servers:

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    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.
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    Apache 2.0
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    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.
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    Apache 2.0
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    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.
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    MIT
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    A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.
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    MIT
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    MCP server for searching files on macOS by name or content using semantic and lexical search, providing file paths and line numbers for agents to read from.
    MIT
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    A lab demonstrating how to deploy a Model Context Protocol (MCP) server and integrate it with Microsoft Copilot Studio, allowing users to connect AI models to different data sources and tools through a standardized protocol.
    MIT
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    Provides semantic search, note retrieval, source explanation, daily digests, and health checks for a local Obsidian vault, enabling MCP clients like Claude Desktop and Claude Code to query the second brain via natural language.
    MIT
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    An MCP server that provides jokes to Microsoft Copilot Studio, demonstrating how to deploy a Model Context Protocol server that delivers Chuck Norris and Dad jokes upon request.
    MIT
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    MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.
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    A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.
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    Exposes any SQLite database as read-only MCP tools for AI assistants, enabling listing tables, describing schemas, and running SELECT queries with filtering, ordering, and pagination.
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    Translates plain English questions about infrastructure operations into SQL queries, executes them against a database, and returns the real answer.