Semantic Scholar

Integrates with Semantic Scholar's academic database to search research papers, retrieve author information, analyze citation networks, and discover paper recommendations for literature reviews and academic research workflows.

This MCP server provides comprehensive access to the Semantic Scholar academic search API, enabling AI assistants to search for research papers, retrieve detailed paper and author information, analyze citation networks, and discover paper recommendations. Built by Alperen Kocyigit using Python with FastMCP, it implements robust retry logic with exponential backoff for rate limiting and includes advanced features like batch operations for multiple papers/authors, text snippet search within papers, autocomplete suggestions, and ML-powered paper recommendations based on positive/negative examples. The implementation is designed for academic research workflows, literature reviews, citation analysis, and building AI research assistants that need to navigate and analyze the scholarly literature landscape.