Stanford's Paper2Agent turns research papers into working AI agents

Stanford researchers published Paper2Agent in Nature, a system that converts research papers into validated Model Context Protocol (MCP) tools that can reproduce a paper's results and run on new data. It scored 91.2% on a benchmark of 300 questions spanning 74 papers.

Why it matters: Turning static papers into runnable, queryable agents could speed up how researchers verify and build on prior work, addressing reproducibility problems in computational science. It's also a concrete, peer-reviewed example of the "paper-to-agent" pattern that MCP tooling has made newly practical.