The PubMed MCP Server acts as a bridge between AI assistants and PubMed's biomedical literature repository using the Model Context Protocol (MCP). It enables AI models to perform programmatic search for scientific articles, retrieve detailed article metadata, and conduct deep analysis within an MCP workflow. Core capabilities include paper search via keywords or advanced queries, fast retrieval of article metadata, and access to full-text content when available. The server also supports targeted analyses, providing deep paper analysis and a set of research prompts to guide interpretation and synthesis. Implemented in Python, the project exposes an MCP server (pubmed_server.py) and supporting search logic (pubmed_web_search.py), with prerequisites like Python 3.10+ and the FastMCP library. Usage includes starting the MCP server with python pubmed_server.py and integrating with clients such as Claude Desktop or other MCP-enabled tools. The MCP tools exposed include search_pubmed_key_words, search_pubmed_advanced, get_pubmed_article_metadata, download_pubmed_pdf, and deep_paper_analysis.