The field of large language models (LLMs) is moving towards more effective integration with external tools, with a focus on improving the reliability and accuracy of tool usage in real-world applications. Researchers are exploring ways to enhance LLMs' ability to process complex tool outputs, detect and correct tool-use errors, and evaluate tool-augmented dialogue systems. Notable papers in this area include: ToolCritic, which introduces a diagnostic framework to detect and correct tool-use errors in dialogue systems, improving tool-calling accuracy by up to 13%. ToolScope, which proposes a method to enhance LLM agent tool use through tool merging and context-aware filtering, resulting in gains of 8.38% to 38.6% in tool selection accuracy.