bioRxiv MCP Server

The bioRxiv MCP server bridges the gap between AI assistants and the vast repository of pre‑prints in biological sciences. By exposing a lightweight, protocol‑compliant interface, it lets models like Claude query bioRxiv directly for relevant literature without needing custom web‑scraping logic. This solves a common pain point for researchers and developers: accessing up‑to‑date, peer‑reviewed research quickly within an AI workflow.

At its core, the server offers a set of intuitive tools that mirror typical research tasks. Users can perform keyword or advanced searches, retrieve detailed metadata (title, authors, abstract, publication date), and download full paper PDFs. Once downloaded, papers are cached locally to reduce latency for subsequent requests—a feature that benefits large‑scale analyses or offline usage. The server also ships a curated set of research prompts, enabling AI assistants to guide users through literature reviews or data extraction without manual intervention.

Key capabilities include:

• Rapid search across bioRxiv’s index, returning results in milliseconds.
• Metadata extraction that normalizes author lists, affiliations, and DOI handling for downstream processing.
• Full‑text access with automatic PDF download and local storage, ensuring repeatable queries.
• Advanced query parameters (e.g., date ranges, subject categories) that let developers fine‑tune results for niche studies.
• Integration hooks for popular AI clients (Claude Desktop, Cursor, Windsurf) via Smithery or direct configuration, making deployment a one‑liner.

Real‑world scenarios that benefit from this server include:

• Literature review assistants that can pull the latest pre‑prints on a given topic and summarize key findings.
• Data extraction pipelines that harvest metadata for large‑scale bibliometric analyses or machine learning training sets.
• Research chatbots that answer domain‑specific questions by referencing the most recent work in genomics, microbiology, or neuroscience.
• Academic workflow automation where a researcher’s IDE or notebook can invoke MCP calls to keep references current without leaving the development environment.

Because it follows the Model Context Protocol, developers can embed the bioRxiv server into any MCP‑compatible platform. The server’s modular design—built on FastMCP and asyncio—ensures low overhead, while the local caching strategy offers a competitive edge over external API calls that may suffer from rate limits or latency spikes. In short, the bioRxiv MCP server turns a sprawling pre‑print archive into a programmable knowledge base that AI assistants can query, interpret, and act upon in real time.