This MCP server provides academic paper search and retrieval functionality across multiple sources like Semantic Scholar and Crossref. Built with Python using the FastMCP framework, it offers tools for searching papers, fetching detailed metadata, and filtering by topic and date range. The implementation focuses on delivering structured academic information through a standardized interface, making it particularly useful for AI assistants and applications that require access to scientific literature. By connecting to established academic APIs, this server enables use cases such as literature reviews, research trend analysis, and citation management, enhancing the capabilities of AI models in academic and research contexts.
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Highlight AISearch for academic papers across multiple sources. Parameters: query (str): Search query text, limit (int, optional): Maximum number of results to return (default: 10). Returns: Formatted string containing paper details.
Retrieve detailed information for a specific paper. Parameters: paper_id (str): Paper identifier (DOI or Semantic Scholar ID), source (str, optional): Data source ('crossref' or 'semantic_scholar', default: 'crossref'). Returns: Formatted string with comprehensive paper metadata including title, authors, year, DOI, venue, open access status, PDF URL (Semantic Scholar only), abstract and TL;DR summary (when available).
Search for papers by topic with optional date range filter. Parameters: topic (str): Search query text (limited to 300 characters), year_start (int, optional): Start year for date range, year_end (int, optional): End year for date range, limit (int, optional): Maximum number of results to return (default: 10). Returns: Formatted string containing search results including paper titles, authors, and years, abstracts and TL;DR summaries when available, venue and open access information.
