Large language models (LLMs) have showcased remarkable capabilities as autonomous agents when augmented with external tools.Equipped with fixed tool sets, LLMs struggle with addressing diverse user inquiries in open-world tasks.To evaluate and boost the performance of LLMs in dealing with complex demands in the real-world, we propose open-world function calling, where LLMs need to retrieve suitable tools from a pre-defined external tool library and use retrieved tools to resolve the user's problem.We introduce Meta-Tool, a versatile and plug-and-play tool retrieval system as the access of LLMs to external tool library.Drawing inspiration from the myriad of enhanced approaches associated with Retrieval-Augmented Generation (RAG), Meta-Tool employs a hypothesizeretrieve-invoke framework.We further propose Meta-Bench, a comprehensive benchmark for evaluating LLMs in open-world function calling and associated tasks.Meta-Bench encompasses 2, 800 dialogues and 7, 361 tools, spanning ten distinct scenarios to provide robust and diverse test categories.In conjunction, we present MT-LLaMA, a finetuned version of LLaMA-3.1, which exhibits remarkable performance improvements.Our empirical experiments reveal that Meta-Tool significantly enhances the ability of advanced LLMs to retrieve and leverage the most suitable tools compared to previous tool retrieval methods.Moreover, our fine-tuning enables even smallersized LLMs to achieve comparable even exceeding results to GPT-4o.Both the benchmark and the model are made publicly available at https://github.com/qinshengqian/Meta-Tool to foster further research and development in the field.