The description of a tool for a large language model (LLM) is crucial for its effective use, acting as a protocol rather than mere documentation. Key details such as when to call the tool, what inputs it accepts, and its behavior after timeouts or failures must be explicitly stated. Failing to provide these specifics can lead to expensive, silent errors where the model makes plausible but incorrect decisions, resulting in weeks of debugging to uncover the root cause. Developers should rigorously review seven key questions before shipping a tool to ensure all necessary information is present in its description, including trigger conditions, boundaries, input formats, return values, latency, cost, and recovery paths. AI
IMPACT Clearer tool descriptions will improve LLM agent reliability and reduce costly errors in AI-powered applications.
RANK_REASON Article discusses best practices for LLM tool descriptions, not a new release or event.
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