In the automotive industry, the design, modeling, and planning of multi-robot cells are manual error-prone, and time-expensive tasks. A recent work investigated, using reactive synthesis, approaches to automate robot task planning, and execution. In this paper, we present a tool that realizes a model-at-runtime approach. The tool is integrated with a robot simulation tool, to automate efficient multi-robot choreography planning, and execution. We illustrate the tool using a multi-robot spot welding cell, inspired from an industrial case. Given a virtual model of the production cell, and user constraints definition, the tool can derive a specification for the GR(1) reactive synthesis. The tool integrates the synthesized controller with the production cell execution, and in real time, optimizes the strategies by considering the uncertainties. The system can select among several correct, and safe actions, the optimal action using AI-based planning techniques, such as the Monte Carlo Tree Search (MCTS) algorithm. We showcase our tool, illustrate its implementation architecture, including how it can support robot experts for automated planning and execution of production cells.