Speaker
Description
Large Language Models (LLMs) can serve as connective elements within ATLAS analysis workflows, linking data-discovery utilities, columnar data-delivery systems, and analysis-level plotting frameworks. Building on earlier exploratory studies of LLM-generated plotting code, we now focus on an implementable architecture suitable for real use. The system is decomposed into reusable Model Context Protocol (MCP) tools that handle key tasks: ATLAS dataset and metadata lookup, luminosity and auxiliary data retrieval, and orchestration of ServiceX with both Pythonic Awkward Array–based analysis and ROOT RDataFrame workflows. A user supplies a high-level request—such as a variable to plot from a given dataset—and the toolset resolves dataset identifiers, fetches required metadata, generates an analysis snippet consistent with ATLAS conventions, and produces a complete plotting workflow. We describe the design of this modular tool layer, the improvements in robustness and determinism over earlier prototypes, and the path toward a lightweight, practical ATLAS plot-generation assistant that can be embedded in broader systems.