Imagine having a vast toolbox at your disposal, but struggling to find the right tool for the job. That's the challenge facing today's AI, especially large language models (LLMs). These powerful AIs excel at various tasks, but they often need external tools to interact with the real world. As the number of tools grows, finding the most relevant one becomes a major bottleneck. Enter Re-Invoke, a groundbreaking new method for zero-shot tool retrieval. This innovative approach doesn't require any training data, making it incredibly scalable and adaptable. Re-Invoke works by first generating a diverse set of synthetic queries that cover different aspects of each tool's function. Think of it as creating a detailed map of the tool's capabilities. Then, during inference, Re-Invoke leverages an LLM's understanding to extract the core intent from a user's query. This helps pinpoint the user's actual need, even if the query is complex or contains extraneous information. Finally, it uses a novel multi-view ranking strategy to match the user's intent with the most relevant tools. The results are impressive. Re-Invoke outperforms state-of-the-art alternatives on standard benchmarks, achieving significant improvements in retrieval accuracy. This breakthrough has real-world implications for AI assistants and chatbots. By efficiently retrieving the right tools, Re-Invoke empowers LLMs to tackle more complex and dynamic tasks. It also paves the way for more robust and adaptable AI systems that can seamlessly integrate new tools without extensive retraining. While promising, Re-Invoke still faces challenges, such as improving the diversity and quality of synthetic queries and refining the intent extraction process. Future research aims to address these limitations and further enhance the efficiency and reliability of tool retrieval in the ever-evolving landscape of AI.