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"A YouTube MCP server with statistics, available via npx or Python command" matching MCP servers:

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    Transforms YouTube into a queryable knowledge source with search, video details, transcript analysis, and AI-powered tools for summaries, learning paths, and knowledge graphs. Features quota-aware API access with caching and optional OpenAI/Anthropic integration for advanced content analysis.
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    MIT
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    This read-only MCP Server allows you to connect to YouTube Analytics data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp
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    MIT
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    A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.
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    MIT
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    Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
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    MIT
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    Enables semantic search across thousands of indexed educational YouTube videos covering topics like engineering, marketing, and strategy. It allows users to query insights from top creators directly through Claude Code using the Model Context Protocol.
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    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
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    Builds a searchable knowledge base from YouTube video transcripts with hybrid semantic and keyword search. Allows LLM assistants to search, organize, and retrieve timestamped information from videos you've watched.
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    Implements the Model Context Protocol (MCP) to provide AI models with a standardized interface for connecting to external data sources and tools like file systems, databases, or APIs.
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    A server that connects Claude to your documentation via Inkeep's API, enabling AI-powered interactions with your documentation content.
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    MIT
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    A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.
    MIT