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    A Model Context Protocol server that provides a scratchpad tool for LLMs to organize thoughts, take notes, and plan approaches to complex problems.
    1
    2 npm
    1
    MIT
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    Enables issuing credit notes and credit memos against an invoice or standalone as revisable drafts that can be finalized into frozen numbered documents, then rendered as Markdown or printable HTML with totals broken down by currency, reason and month. All amounts are handled as integer minor units and all data stays local, so nothing is sent anywhere.
    10
    MIT
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    Bridges Youdao Note's official SSE MCP endpoint to local stdio, dynamically exposing all official tools to MCP clients so users can manage notes, todos, search, and clipping through natural language.
    3
    MIT
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    Enables AI assistants to discover and invoke read-only EVE Online ESI API endpoints, retrieve public and character data with automatic OAuth, and turn that context into practical adventure plans.
    3
    62 npm
    AGPL 3.0
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    A line-oriented text file editor. Optimized for LLM tools with efficient partial file access to minimize token usage.
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    MIT
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    Enables AI clients to retrieve verified countdown event dates, semantics, and FAQ from Countdowns Online, allowing users to browse event categories and understand date contexts without API keys.
    2
    MIT
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    The Universal MCP Server exposes tools for automated posting and draft saving to note.com. It reads Markdown files containing titles, body text, and tags, then publishes them to your note.com account using Playwright automation.
    2
    22 npm
    MIT
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    Provides a tool to calculate basic text metrics including character count, characters without spaces, and word count.
    1
    MIT
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    MCP server for ATOL Online fiscal receipt API. Enables 54-FZ compliant receipt generation, refunds, corrections, and status checks for Russian online cash registers.
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    9 npm
    MIT
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    Provides 9 text processing tools for analysis, sentiment, language detection, summarization, spelling, and readability tips.
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    Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.
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