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    A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.
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    MIT
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    A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.
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    MIT
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    Enables agents to check and retrieve concept-structured YouTube video summaries from the oratilo library via lookup and full-text search tools, avoiding redundant summarization work.
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    MIT
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    An MCP server that enables searching arXiv, importing papers, enriching with citation data, generating summaries and research ideas, and querying a local library using local LLMs and embeddings.
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    MIT
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    Unofficial MCP server wrapping crawl4ai that enables extraction and analysis of content from web pages, PDFs, Office documents, YouTube videos, and more, with AI-powered summarization and Google search integration to reduce token usage while preserving key information.
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    MIT
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    Provides MCP tools for querying and summarizing Northwest A&F University campus information, including trending QQ channel posts, official website notices/events/competitions, and cross-site custom searches with Markdown output and source links.
    4
    MIT
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    distill-mcp-v2 is a high-performance, network-dependency-free Python FastMCP server designed to aggressively optimize Large Language Model (LLM) context windows. It provides specialized tools for compressing and analyzing massive AI-agent payloads without losing critical semantic information.
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    MIT
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    Enables Chinese text segmentation using the jieba library with support for precise, full, and search engine modes. Provides natural language access to professional Chinese word segmentation capabilities through MCP protocol.
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    MIT
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    A note management MCP server that enables users to store, retrieve, and summarize personal notes via a custom URI scheme. It provides tools for adding content and prompts for generating brief or detailed summaries of stored information.
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    Enables parsing of raw resume text into structured JSON format with organized sections for skills, experience, education, and projects. Uses Google's Gemini AI model to extract and categorize resume information from unstructured text input.