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A system for retrieving medical knowledge, especially in medical imaging, and generating reports

  • Why this server?

    Provides RAG capabilities for semantic document search, allowing users to add, search, list, and delete documentation with metadata support, which is useful for retrieving specific medical knowledge.

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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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    4
    TypeScript
    Apache 2.0
  • Why this server?

    Provides unified access to multiple search engines and AI tools, enabling comprehensive retrieval of information for medical knowledge.

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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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    76
    61
    TypeScript
    MIT License
    • Linux
  • Why this server?

    Provides Claude with read-only access to Hugging Face Hub APIs, enabling interaction with models, datasets, spaces, papers, and collections through natural language.

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    A Model Context Protocol server that provides Claude and other LLMs with read-only access to Hugging Face Hub APIs, enabling interaction with models, datasets, spaces, papers, and collections through natural language.
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    4
    Python
    MIT License
    • Apple
  • Why this server?

    Enables real-time search and retrieval of academic paper information from multiple sources, providing access to paper metadata, abstracts, and full-text content when available.

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    Enables real-time search and retrieval of academic paper information from multiple sources, providing access to paper metadata, abstracts, and full-text content when available, with structured data responses for integration with AI models that support tool/function calling.
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    11
    Python
    AGPL 3.0
  • Why this server?

    A powerful Model Context Protocol framework that extends Cursor IDE with tools for web content retrieval, PDF processing, and Word document parsing, which could be used to parse and summarize medical articles.

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    A powerful Model Context Protocol framework that extends Cursor IDE with tools for web content retrieval, PDF processing, and Word document parsing.
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    8
    8
    Python
    MIT License
    • Linux
    • Apple
  • Why this server?

    Provides document processing capabilities, allowing conversion of documents to markdown and extraction of tables, potentially useful for report generation.

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    A server that provides document processing capabilities using the Model Context Protocol, allowing conversion of documents to markdown, extraction of tables, and processing of document images.
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    6
    Python
    MIT License
    • Linux
    • Apple
  • Why this server?

    A Model Context Protocol server that provides text-to-speech capabilities using the Kokoro TTS model, offering multiple voice options and customizable speech parameters, which could be used to generate reports.

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    A Model Context Protocol server that provides text-to-speech capabilities using the Kokoro TTS model, offering multiple voice options and customizable speech parameters.
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    239
    JavaScript
    • Apple
    • Linux
  • Why this server?

    An MCP server to run commands, which could potentially be configured to run reporting tools and access information if user configures it.

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    An MCP server to run commands.
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    446
    138
    TypeScript
    MIT License
    • Apple
  • Why this server?

    Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities, allowing more nuanced search for medical imaging information.

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    Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.
    Last updated -
    1
    JavaScript
    MIT License