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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 task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.
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    MIT
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    MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.
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    The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con
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    Enables exploring Steam store reviews and community discussion threads with server-side filtering, temporal analysis, and metadata context for understanding player sentiment and feedback beyond store page noise.
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    BSD 3-Clause
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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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    GPL 3.0
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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 context compression for Claude Code with Thai language support, reducing token usage by 50-88% while preserving full recoverability of original content.
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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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    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