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 (string), limit (optional int)
Retrieve detailed information for a specific paper. Parameters: paper_id (string), source (optional string)
Search for papers by topic with optional date range filter. Parameters: topic (string), year_start (optional int), year_end (optional int), limit (optional int)