Semantic Scholar MCP Server

The Semantic Scholar MCP Server bridges the powerful Semantic Scholar research database with AI assistants that support the Model Context Protocol. By exposing a set of focused tools—searching for papers, retrieving detailed paper and author information, and gathering citations and references—the server enables developers to enrich conversational agents with up-to-date scholarly data without handling authentication or API rate limits themselves.

At its core, the server runs a lightweight MCP listener that translates incoming requests into calls to the Semantic Scholar REST API via the Python package. Each tool is wrapped in a clear, declarative interface: accepts a query string and returns a ranked list of relevant papers; fetches metadata such as title, abstract, venue, and citation metrics; pulls author profiles and publication histories; and provides a paper’s citation graph. This modularity allows AI assistants to compose complex research workflows—such as automatically generating literature reviews or tracking the evolution of a scientific topic—by chaining tool calls.

For developers, this server offers several tangible benefits. First, it abstracts away the intricacies of Semantic Scholar’s API, including pagination and rate limiting, allowing assistants to focus on natural language understanding. Second, the MCP integration means any client that understands MCP—Claude Desktop, Cursor, Windsurf, or custom tools—can invoke these capabilities with a single line of JSON. Third, the server’s design supports easy scaling: additional Semantic Scholar endpoints can be added without modifying client code. Finally, the MIT license and open‑source nature encourage community contributions, making it straightforward to adapt or extend the toolset for niche domains such as conference proceedings or preprint repositories.

Typical use cases include academic research assistants that automatically surface the latest papers on a user‑specified topic, citation analysis bots that map influence networks for grant proposals, or educational tools that fetch author biographies to personalize learning materials. By integrating this MCP server into existing AI workflows, developers can deliver richer, data‑driven interactions that keep pace with the rapidly evolving scholarly landscape.