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🎓 Semantic Scholar MCP Server

🔍 This project implements a Model Context Protocol (MCP) server for interacting with the Semantic Scholar API. It provides tools for searching papers, retrieving paper and author details, and fetching citations and references.

Created by JackKuo6662025/03/27
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What is 🎓 Semantic Scholar MCP Server?

What is Semantic Scholar MCP Server? The Semantic Scholar MCP Server is a project that implements a Model Context Protocol (MCP) server for interacting with the Semantic Scholar API, enabling users to search for academic papers, retrieve details about papers and authors, and fetch citations and references. How to use Semantic Scholar MCP Server? To use the server, you need to install the required Python packages and run the server script. After starting the server, you can interact with it using an MCP client to access various tools for searching and retrieving academic information. Key features of Semantic Scholar MCP Server? 🔍 Search for papers on Semantic Scholar 📄 Retrieve detailed information about specific papers 👤 Get author details 🔗 Fetch citations and references for a paper Use cases of Semantic Scholar MCP Server? Academic research: Quickly find relevant papers for your research topic. Citation analysis: Retrieve citations and references for a specific paper to understand its impact. Author profiling: Get detailed information about authors and their publications. FAQ from Semantic Scholar MCP Server? Can I use this server for any academic field? Yes! The server can be used to search for papers across various academic disciplines available in the Semantic Scholar database. What are the prerequisites for running the server? You need Python 3.10+ and the semanticscholar and mcp Python packages installed. Is there a graphical interface for this server? No, the server is designed to be used via an MCP client, which requires command-line interaction.

As an MCP (Model Context Protocol) server, 🎓 Semantic Scholar MCP Server enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

How to use 🎓 Semantic Scholar MCP Server

To use the server, you need to install the required Python packages and run the server script. After starting the server, you can interact with it using an MCP client to access various tools for searching and retrieving academic information. Key features of Semantic Scholar MCP Server? 🔍 Search for papers on Semantic Scholar 📄 Retrieve detailed information about specific papers 👤 Get author details 🔗 Fetch citations and references for a paper Use cases of Semantic Scholar MCP Server? Academic research: Quickly find relevant papers for your research topic. Citation analysis: Retrieve citations and references for a specific paper to understand its impact. Author profiling: Get detailed information about authors and their publications. FAQ from Semantic Scholar MCP Server? Can I use this server for any academic field? Yes! The server can be used to search for papers across various academic disciplines available in the Semantic Scholar database. What are the prerequisites for running the server? You need Python 3.10+ and the semanticscholar and mcp Python packages installed. Is there a graphical interface for this server? No, the server is designed to be used via an MCP client, which requires command-line interaction.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.

Use Cases for this MCP Server

  • No use cases specified.

MCP servers like 🎓 Semantic Scholar MCP Server can be used with various AI models including Claude and other language models to extend their capabilities through the Model Context Protocol.

About Model Context Protocol (MCP)

The Model Context Protocol (MCP) is a standardized way for AI agents to communicate with various services and tools. MCP servers like 🎓 Semantic Scholar MCP Server provide specific capabilities that can be accessed through a consistent interface, making it easier to build powerful AI applications with complex workflows.

Browse the MCP Directory to discover more servers and clients that can enhance your AI agents' capabilities.