Workflow
Claude 官方发文:如何给 Agent 构建一个好用的工具?
Founder Park·2025-09-12 10:06

Core Insights - Anthropic has introduced new features in Claude that allow direct creation and editing of various mainstream office documents, expanding AI's application scenarios in practical tasks [2] - The company emphasizes the importance of designing intuitive tools for uncertain, reasoning AI rather than traditional programming methods [4] - A systematic evaluation of tools using real and complex tasks is essential to validate their effectiveness [5] Group 1 - The focus is on creating integrated workflow tools rather than isolated functionalities, which significantly reduces the reasoning burden on AI [6] - Clear and precise descriptions of tools are crucial for AI to understand their purposes, enhancing the success rate of tool utilization [7] - The article outlines key principles for writing high-quality tools, emphasizing the need for systematic evaluation and collaboration with AI to improve tool performance [13][36] Group 2 - Tools should be designed to reflect the unique affordances of AI agents, allowing them to perceive potential actions differently than traditional software [15][37] - The article suggests building a limited number of well-designed tools targeting high-impact workflows, rather than numerous overlapping functionalities [38] - Naming conventions and namespaces are important for helping AI agents choose the correct tools among many options [40] Group 3 - Tools should return meaningful context to AI, prioritizing high-information signals over technical identifiers to improve task performance [43] - Optimizing tool responses for token efficiency is crucial, with recommendations for pagination and filtering to manage context effectively [48] - The article advocates for prompt engineering in tool descriptions to guide AI behavior and improve performance [52] Group 4 - The future of tool development for AI agents involves shifting from predictable, deterministic patterns to non-deterministic approaches [54] - A systematic, evaluation-driven method is essential for ensuring that tools evolve alongside increasingly powerful AI agents [54]