Large language models (LLMs) have demonstrated remarkable zero-shot reasoning abilities in a variety of domains. In robotics, recent works have begun to leverage LLMs for planning and control. However, integrating physical feedback modalities (such as haptic force sensing) into the LLM reasoning loop remains underexplored. In this paper, we present a vision-language-action robotic system that employs an LLM to decide actions based on both visual and haptic feedback, without task-specific training. The system consists of a 7-DoF Franka Emika Panda robot equipped with an Intel RealSense D435 RGB-D camera and a Wittenstein HEX-21 6-axis force/torque sensor. The LLM (OpenAI “o3-mini”) receives real-time visual and force sensor data as context and issues high-level actions (press or hold) to manipulate a “faulty button” until its LED turns green (indicating a successful press). We focus on the zero-shot reasoning capability of the LLM in this multimodal feedback loop. Experimental results show that the LLM can successfully interpret the combined sensory inputs to achieve the task, with success rates outperforming a fixed reactive policy. We analyze example reasoning traces to illustrate how the model dynamically decides when to press repeatedly or maintain pressure. The proposed approach highlights the potential of LLM-based reasoning in robotic manipulation tasks requiring multimodal feedback integration.