The article outlines advancements in AI tool usage, particularly focusing on Claude's ability to work with a vast number of tools efficiently. The goal is to create AI agents that can interact with numerous tools without overwhelming their context capacity. Traditional methods load all tool definitions upfront, leading to excessive token consumption—up to 134,000 tokens in some cases. This creates challenges like incorrect tool selection and parameter errors. To address these issues, the new Tool Search Tool allows Claude to discover tools on-demand, significantly reducing context consumption from around 77,000 tokens to about 8,700 tokens, thereby preserving valuable processing space. In addition to the Tool Search Tool, two other features are introduced: Programmatic Tool Calling and Tool Use Examples. Programmatic Tool Calling reduces the context pollution from intermediate results by allowing Claude to orchestrate tools through code. This avoids the need for multiple inference passes and keeps only relevant data in context. Tool Use Examples provide a framework for understanding how to use tools properly, moving beyond simple schema definitions to include practical usage patterns. The enhancements lead to improved accuracy in tool selection and performance, as seen in internal tests where performance metrics jumped significantly when using the new features. These innovations collectively aim to make AI agents like Claude more versatile and efficient, enabling them to handle complex workflows without the typical constraints of traditional tool use. By optimizing token usage and improving accuracy, these tools can expand what developers can achieve with AI in practical applications.