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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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    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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    ✨ 为你的 AI 助手装上 B 站的眼睛:一键提取视频字幕与热门评论,助力高效信息总结 🚀 ✨ Equip your AI assistant with "Bilibili Eyes": One-click extraction of video subtitles and popular comments for efficient information summarization 🚀
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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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    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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    Provides a suite of regex and text processing tools for AI agents, including pattern testing, extraction, and replacement with capture group support. It also enables various text transformations like case conversion, line sorting, and deduplication through the Model Context Protocol.
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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.
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