ArXiv MCP Server

The ArXiv MCP Server bridges the gap between AI assistants and the vast repository of scholarly work on arXiv. By exposing a simple, standardized Model Context Protocol interface, it removes the need for developers to build custom scrapers or API wrappers. Instead, an AI model can issue high‑level queries—such as “find the latest papers on transformer architectures in computer vision”—and receive structured results that include titles, authors, abstracts, and download links. This eliminates the friction of manual research, enabling rapid iteration in academic workflows, literature reviews, and data‑driven experimentation.

At its core, the server offers four practical tools that cover the entire research cycle. First, Paper Search lets assistants query arXiv with flexible filters for keywords, date ranges, and subject categories. Second, Paper Download retrieves the PDF (or source) for a given arXiv ID and stores it locally, ensuring quick subsequent access. Third, List Papers enumerates all stored documents, providing a ready inventory for further processing or citation. Finally, Read Paper extracts the text of a downloaded PDF into a machine‑readable format. Together, these tools give developers a full, end‑to‑end solution for integrating scholarly literature into AI‑powered applications.

The server’s local storage strategy is a key differentiator. By persisting papers on disk, it reduces repeated network calls and speeds up downstream tasks such as natural language processing or citation analysis. Developers can configure the storage path, allowing integration with existing data pipelines or cloud‑based file systems. Moreover, the server ships a curated set of Research Prompts—templated queries that help users frame common academic tasks, from summarizing a paper to generating literature review outlines. These prompts reduce the learning curve for non‑technical users and standardize interactions across different AI assistants.

Real‑world use cases span academia, industry research labs, and data science teams. A researcher can let an assistant automatically pull the latest papers on a niche topic, summarize key findings, and identify gaps for a new project. A data scientist might embed the server into an automated pipeline that continuously feeds fresh research papers to a language model for knowledge graph construction. In education, tutors could use the server to retrieve relevant papers and generate study guides on demand. Because the MCP interface is language‑agnostic, any model that understands MCP—Claude, GPT‑4o, or custom LLMs—can tap into arXiv without bespoke code.

Integration is straightforward within existing MCP workflows. Developers add a single configuration entry pointing to the server’s executable, optionally passing a storage path. Once registered, the assistant can invoke the four tools via standard MCP calls, receiving JSON‑encoded results that are immediately usable. This plug‑and‑play approach means teams can start leveraging arXiv content in minutes, scaling from a single developer to large collaborative projects without maintaining separate services or dealing with API rate limits.