A tool can accept an input, finish without an exception, and return a convincing explanation while leaving the most important scientific question unresolved: what exactly was predicted, using which materials and method?

Paper2Agent makes that boundary worth examining. The authors connect research papers and their original code to callable tools, tests and natural-language use. Their public TISSUE example names three input files and requests a 95% prediction interval for Acta2. That is a concrete task with a reviewable chain, rather than an invitation to ask an unconstrained chatbot for a biological conclusion.

This article explains that example and gives a small metadata checker you can run locally. It does not install Paper2Agent, generate an MCP server, read research matrices, train a model or reproduce the authors’ result.…