MCP agents in PanDA

P. Nilsson Joseph Boudreau Tasnuva Chowdhury Shengyu Feng Adolfy Hoisie Raees Khan Jaehyung Kim Ozgur O. Kilic S. Klasky A. Klimentov T. Korchuganova T. Maeno V. M. Outschoorn D. Park N. Podhorszki Yi-Hui Ren F. Suter Sairam Sri Vatsavai T. Wenaus R. Xue Wei Yang Yiming Yang Shinjae Yoo

Jul 2026 · Journal of Instrumentation · Vol 21 · 0 citations · 3 references

Physics

Abstract

The PanDA workload management system, developed for large-scale distributed computing in high-energy physics, is being enhanced through the integration of AI-assisted operational tools built on the Model Context Protocol (MCP). This paper describes two complementary efforts. The first is PanDA MCP, a FastAPI-based interface layer that exposes PanDA REST APIs as standardized, self-describing MCP tools, bridging the synchronous PanDA backend with asynchronous AI clients. The second is Bamboo MCP, a modular plugin-based toolkit for AI-assisted operations, whose ATLAS plugin implements AskPanDA — a natural-language interface to the PanDA workload management system. Bamboo adopts a tool-first, evidence-driven architecture in which deterministic routing and structured data retrieval precede any LLM invocation. A key new capability enables natural-language queries against a live PanDA job database via an LLM-generated SQL pipeline protected by an AST-based security guard. A supervisor-managed suite of background agents maintains the local data stores on which these tools depend. The toolkit is experiment-agnostic by design, with plugins for ePIC, the Vera Rubin Observatory, and CGSim planned. A GPU-based testbed at Brookhaven National Laboratory supports co-development across the ATLAS and EIC communities.

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