Provides Google Scholar search capabilities through a streamable HTTP Model Context Protocol (MCP) server.
Sponsored
Serves as a Model Context Protocol (MCP) server, offering Google Scholar search functionalities via a streamable HTTP transport. This innovative tool enables AI models, such as Google's Gemini, to directly access and utilize academic research data by acting as an extensible bridge, allowing for the creation of AI agents capable of intelligent academic query processing and information retrieval. It demonstrates how to build an MCP server with custom tools and integrate them seamlessly with advanced AI models.
Key Features
01Seamless integration with Google Gemini AI models for enhanced capabilities
02Extensible tool system with a dedicated `search_google_scholar` tool
03Streamable HTTP Transport for efficient communication
04Multi-session support for handling simultaneous client connections
05Persistent conversation context and function calling for AI-driven interactions
060 GitHub stars
Use Cases
01Enabling conversational AI agents to answer academic queries with real-time Google Scholar data.
02Facilitating AI model access to academic literature for research and data retrieval.
03Developing custom AI-powered research assistants that can search and synthesize academic papers.
