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A service for interpreting files and PSF documents to generate insights and summaries

  • Why this server?

    Dify can interpret files and documents through its workflow capabilities, and MCP allows invoking these workflows.

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    Server for using Dify. It achieves the invocation of the Dify workflow by calling the tools of MCP.
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    Python
  • Why this server?

    Offers unified access to multiple search engines and AI tools for content processing, which can be used to analyze documents.

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    🔍 A Model Context Protocol (MCP) server providing unified access to multiple search engines (Tavily, Brave, Kagi), AI tools (Perplexity, FastGPT), and content processing services (Jina AI, Kagi). Combines search, AI responses, content processing, and enhancement features through a single interface.
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    TypeScript
    MIT License
    • Linux
  • Why this server?

    Enables LLMs to search, retrieve, and manage documents through Rememberizer's knowledge management API.

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    A Model Context Protocol server enabling LLMs to search, retrieve, and manage documents through Rememberizer's knowledge management API.
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    Python
    Apache 2.0
  • Why this server?

    Enables document format conversion, which could be useful for preparing files for interpretation and summarization.

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    MCP server for seamless document format conversion using Pandoc, supporting Markdown, HTML, PDF, DOCX (.docx), csv and more.
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    104
    Python
    MIT License
    • Apple
  • Why this server?

    Facilitates searching and reading PDF and Word files in Box, enabling content access for interpretation.

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    The Box MCP Server facilitates searching and reading PDF and Word files in Box using Developer Token authentication.
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    2
    JavaScript
    BSD 3-Clause
  • Why this server?

    Provides tools for reading, writing, and editing files on the local filesystem.

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    An MCP server that provides tools for reading, writing, and editing files on the local filesystem.
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    1,099
    Python
    Apache 2.0
    • Apple
  • Why this server?

    Provides RAG capabilities for semantic document search using Qdrant, which can be useful for identifying key information in documents.

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    Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
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    TypeScript
    Apache 2.0
  • Why this server?

    Enables running SQL queries and analyzing data, and generating business insight memos, which relate to creating summaries and insights.

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    A Model Context Protocol (MCP) server implementation that provides database interaction and business intelligence capabilities through SQLite. This server enables running SQL queries, analyzing business data, and automatically generating business insight memos.
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    37,968
    JavaScript
    MIT License
  • Why this server?

    Provides tools for merging, extracting, and searching PDF files.

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    mcp using PyPDF2 to: • merge-pdfs • extract-pages • search-pdfs • merge-pdfs-ordered (merge in user spec. order) • find-related-pdfs (regex extracted text for related PDF files)
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    19
    Python
    The Unlicense
  • Why this server?

    AI Summarization, Support for multiple content types: Plain text, Web pages, PDF documents, EPUB books, HTML content

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    MCP Server for AI Summarization, Support for multiple content types: * Plain text * Web pages * PDF documents * EPUB books * HTML content
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    JavaScript