Reproduce “Experiences with Model Context Protocol Servers for Science and High Performance Computing” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Haochen Pan, Ryan Chard, Reid Mello, Christopher Grams, Tanjin He, Alexander Brace, Owen Price Skelly, Will Engler and 7 more
Researchers from the University of Chicago and Argonne National Laboratory implemented "thin" Model Context Protocol (MCP) servers to enable AI agents to interact with existing, mature scientific cyberinfrastructure like Globus services and major HPC facilities. This approach successfully orchestrated complex, multi-site scientific workflows, including molecular structure optimization and multi-site phylogenetic analysis, while demonstrating agents' ability to dynamically generate code and self-correct from errors.
Key insights
- Building "thin" MCP adapters over mature, existing research services is highly effective, leveraging established robustness and avoiding redundant development efforts.
- Dynamically identifying and exposing relevant tools from large ecosystems, as demonstrated by the RAG-based Rhea MCP server, provides a scalable solution for tool discovery.
- AI agents can effectively generate "glue code" on demand to integrate diverse scientific applications and manage complex, heterogeneous workflows.
- Agents demonstrated a notable capacity for self-correction, diagnosing and recovering from errors by processing feedback from invoked services.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.