Semantic Scholar FastMCP Server is designed to streamline academic research by exposing a RESTful API that sits between your application and the Semantic Scholar database. It orchestrates multiple prompt chains (MCP) in parallel—such as metadata retrieval, abstract summarization, citation extraction, and question answering—to produce fully processed results in a single response. Developers can configure each chain’s parameters, swap out language models, or add custom handlers, enabling rapid deployment of literature review assistants, research chatbots, and domain-specific knowledge pipelines without building complex orchestration logic from scratch.

The AI Researcher agent acts as a virtual research assistant that automates key phases of scientific inquiry. It begins by accepting a user-defined topic and performing automated literature searches across online databases via integrated web search. It then extracts and summarizes the most relevant papers, highlights core findings, and identifies research gaps. Using these insights, the agent generates novel research questions and proposes experimental design outlines. The framework supports customizable task pipelines, allowing users to adjust search parameters, summarization depth, and idea generation strategies. All interactions occur through a simple command-line interface, leveraging Python scripts and OpenAI APIs. Researchers can review, refine, and export results to accelerate literature reviews and early-stage planning.