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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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    tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.
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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 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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    MCP server for HumanPen that lets AI agents work on real documents (.docx, .pptx, .pdf) — humanizing content to lower AI-detection scores, converting citations, condensing, and translating while preserving formatting, tables, images, and citations.
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    Apache 2.0
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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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    A privacy-first, zero-cost AI assistant that lets you chat with your documents locally via CLI or web app, supporting search, summarization, and Q&A using your own model.
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    Enables AI-generated text detection using the GPTZero API. Provides confidence scores and detailed analysis to identify whether text was written by humans or AI, with multilingual support.
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    MIT