Better Tool Descriptions, Not More Tools, Fix Most AI Agent Failures

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A software developer analyzing AI agent failures found that roughly 40% of errors stemmed from incorrect tool selection rather than flawed reasoning by the model. The core issue is that tool descriptions typically explain what a tool accepts but not when to use it versus a similar alternative. The author proposes a structured description format that includes purpose, use-case conditions, explicit do-not-use scenarios with redirects to correct tools, side effects, and reversibility. When selection errors occur, the recommended fix is updating individual tool descriptions rather than patching the system prompt, as prompt-level fixes are global, conflict-prone, and break across model upgrades. The piece also notes that pruning irrelevant tool schemas before each agent step can meaningfully reduce token costs.

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