The Google Scholar MCP Server provides a bridge between AI assistants and Google Scholar through the Model Context Protocol (MCP). It enables models to search for academic papers and access content programmatically, returning metadata and author information to support literature discovery and analysis. Core features include paper search with custom strings or advanced search parameters, efficient retrieval of paper metadata, author information, and research support to streamline scholarly workflows within MCP-enabled applications. The server exposes three MCP tools—search_google_scholar_key_words, search_google_scholar_advanced, and get_author_info—each accepting parameters such as query, author, year_range, and num_results, and returning lists of article dictionaries or author details. The project comprises a FastMCP-based server implementation (google_scholar_server.py) and a web scraping module (google_scholar_web_search.py). Dependencies include Python 3.10+, mcp[cli]>=1.4.1, scholarly>=1.7.0, and asyncio>=3.4.3. Quick-start instructions cover manual installation, Smithery deployment, and integration examples for MCP-enabled tools in AI assistants.