Agents based on Large Language Models (LLM) have introduced a new way of information seeking that could simplify the search process to suit children’s cognitive skills, as these agents often respond to natural language inquiries with easy-to-read and plausible answers. Still, with emotions playing a crucial role in children’s information seeking and consumption behaviours, it is important to consider whether these agents suit children’s emotional intelligence. With that in mind, in this work, we examine the emotional undertones of LLM agent responses for children’s inquiries. Considering the known impact of prompt engineering on an agent’s response, we investigate whether explicitly informing an agent that the user is a child influences the emotions conveyed in its response. Outcomes from this empirical study reveal the limitations of LLM agents to fit children’s emotional intelligence, with agents tending to over-amplify any underlying emotion in a child’s inquiry. With our findings, we advance knowledge in the role of emotions in children’s online search and offer insights that could be used to improve children’s online information access.