In this paper, we examine the adaptability of a multi-robot coordination system in which distributed autonomous robots respond to unexpected situations based on functional expressions using large language models (LLMs). In recent years, there has been growing interest in systems where multiple autonomous distributed construction machines (robots) collaborate and adaptively perform tasks in open and unknown environments, including disaster sites. In open environments, unforeseen events that cannot be predicted in advance may occur, and it is challenging to address these events solely with existing model-based approaches. In this paper, we leverage the high environment comprehension capabilities of foundation models to understand unforeseen situations and develop a system that enables adaptive coordinated actions by flexibly integrating the functions of multiple robots using LLMs. Additionally, we account for the embodiment (interactions between the robots and their environment). Our experiments demonstrate that the designed system is capable of adaptively responding to three types of unforeseen situations, including path obstructions caused by either an obstacle or a robot. In cases of path obstructions caused by obstacles of varying weights, the system can exhibit appropriate obstacle removal behaviors by reflecting the torque capability of the robot as one aspect of its embodiment.