Semantic Scholar MCP Server

Overview

The Semantic Scholar MCP Server bridges the gap between AI assistants and academic research by exposing the rich capabilities of the Semantic Scholar API through the Model Context Protocol. It resolves a common pain point for developers: accessing scholarly metadata—such as paper abstracts, citation networks, and author profiles—in a structured, programmatic way that AI tools can consume directly. By encapsulating complex HTTP calls behind simple MCP tools, the server lets developers focus on building intelligent workflows rather than wrestling with API authentication or pagination logic.

At its core, the server offers four primary tools: searching for papers by keyword, retrieving detailed paper information (title, authors, venue, abstract), fetching author profiles (affiliation, publication list), and obtaining citation and reference graphs for a given paper. These tools are designed to be idempotent and stateless, ensuring that each call returns fresh data without side effects. The server runs as a lightweight Python process and listens for MCP requests, making it easy to integrate with any client that supports the protocol—Claude Desktop, Cursor, Windsurf, or custom scripts.

Key features include:

• Search-as-a-service: Quickly locate relevant literature using natural language queries, returning structured results that can be fed directly into an AI assistant’s reasoning chain.
• Rich metadata retrieval: Access comprehensive paper and author details, enabling downstream tasks such as summarization, trend analysis, or recommendation generation.
• Citation network exploration: Pull both citations and references for a paper, facilitating graph‑based analyses like impact scoring or co‑citation clustering.
• Seamless client integration: The MCP interface abstracts away authentication and rate limiting, allowing developers to invoke these tools from within prompts or custom workflows without additional boilerplate.

In practice, this server empowers scenarios such as:

• Academic writing assistants that can pull the latest papers on a topic, summarize key findings, and suggest citations in real time.
• Research analytics platforms that aggregate citation networks to identify emerging research fronts or influential authors.
• Educational tools that let students explore literature landscapes, discover related works, and understand the evolution of a field.

By providing a clean, protocol‑driven interface to Semantic Scholar’s vast dataset, the MCP server becomes a versatile component in any AI‑augmented research pipeline, eliminating manual API handling and enabling developers to build richer, more informed applications.