Paper Search

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This Python-based Model Context Protocol (MCP) server empowers large language models and researchers by providing seamless access to academic literature. It enables searching and downloading papers from a multitude of sources like arXiv, PubMed, bioRxiv, and Semantic Scholar, returning results in a standardized format. Integrating with LLM clients such as Claude Desktop, this tool enhances AI-driven workflows, making deep research more efficient and accessible.

Características Principales

• Multi-Source Support: Search and download papers from platforms including arXiv, PubMed, bioRxiv, Google Scholar, and Semantic Scholar.
• MCP Integration: Designed for seamless compatibility with Model Context Protocol clients to enhance LLM capabilities.
• Asynchronous Tools: Efficiently handles network requests for fast and responsive data retrieval.
• Deep Research Ready: Provides standardized 'search' and 'fetch' tools required by OpenAI Deep Research and ChatGPT connectors.
• 2 GitHub stars
• Standardized Output: Papers are consistently returned in a dictionary format via the Paper class for easy processing.

Casos de Uso

• Enhancing large language model (LLM) capabilities with real-time academic paper search and download functionalities.
• Automating literature reviews for researchers by programmatically accessing and collecting papers from multiple academic databases.
• Integrating academic paper search and retrieval into AI-driven research and analysis workflows.