Pubmed_Search
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This project is a Model Context Protocol (MCP) server designed to handle medical queries by searching and analyzing research papers from PubMed. Key features include relevance and impact factor analysis, with results generated in an EndNote-compatible format, aiding in effective literature management.
PubMed Search MCP Server
A Model Context Protocol (MCP) server that analyzes medical topic questions to search for related papers on PubMed and provides results in EndNote format based on relevance and journal Impact Factor.
Key Features
- Search Query Analysis: Analyzes medical-related questions and searches medical papers via the PubMed API
- Relevance Analysis: Selects the most relevant papers based on the title, abstract, and MeSH terms
- Impact Factor Sorting: Sorts papers by journal Impact Factor to provide influential research
- EndNote Format Output: Outputs selected papers in EndNote-compatible format for easy literature management
- Result Classification: Provides results in three files
- List of highly relevant papers
- List of papers with high Impact Factor
- List of papers from journals not in the Impact Factor database