The recent advancements in the integration of external tools with Large Language Models (LLMs) have significantly enhanced their capabilities, transforming them from basic conversational agents into versatile assistants. The field is currently focused on developing datasets and benchmarks that facilitate the effective use of tools by LLMs, addressing challenges such as multi-turn interactions, nested tool calls, and reranking strategies. Notably, there is a strong emphasis on creating open-source resources to promote transparency and collaboration within the research community. These developments are pushing the boundaries of LLM functionality, particularly in areas requiring complex reasoning and real-world application scenarios. The introduction of benchmarks like MTU-Bench and NesTools highlights the need for comprehensive evaluations that simulate real-world tool usage, while innovations like Toolken+ and BUTTON aim to improve tool selection and multi-turn function calling. Additionally, the exploration of circuit-based hypotheses in LLMs offers a deeper understanding of their internal mechanisms, contributing to more efficient and effective model design.