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"Importing FastMCP from the mcp.server Python Module" matching MCP servers:

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    Provides translation capabilities using the DeepL API, supporting text translation between numerous languages, rephrasing, batch translation, document translation, and language detection with formality control.
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    MIT
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    MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.
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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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    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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    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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    Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.
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    MIT
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    A Python MCP server for fetching and summarizing news articles from the Boring News API by date, category, tags, people, and similarity.
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    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