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    A Model Context Protocol server that provides tools for analyzing text documents, including counting words and characters. This server helps LLMs perform text analysis tasks by exposing simple document statistics functionality.
    1
    22 npm
    10
    Apache 2.0
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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.
    2
    MIT
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    Provides detailed character analysis for text, counting total characters, characters without spaces, letters, numbers, and symbols through a simple MCP tool interface.
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    Enables natural language querying of Microsoft Fabric Data Warehouses with intelligent SQL generation, metadata exploration, and business-friendly result summarization. Features two-layer architecture with MCP-compliant server and agentic AI reasoning for production-ready enterprise data access.
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    A minimal MCP server for text processing, providing word count, uppercase conversion, and a summarization prompt template.
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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.
    7
    3
    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.
    1
    2 npm
    1
    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.
    2
    2
    MIT
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    MCP server for koreafilings.com — AI-summarized Korean DART (전자공시) corporate disclosures, paid per call in USDC via the x402 protocol on Base. Tools: get_pricing (free), get_disclosure_summary (0.005 USDC).
    5
    MIT
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    Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.
    4
    MIT
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    Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.
    2
    MIT