How does an AI agent use tool schemas to decide which tool to call?

An agent's reasoning engine, typically the underlying LLM, selects a tool based purely on the tool's declared name and description, without any visibility into how that tool is actually implemented.

• Each available tool exposes a schema describing its purpose and expected parameters
• The agent compares its current goal against these descriptions to decide which tool is most relevant
• Because the model can only see the schema, not the implementation, that schema effectively functions as the contract between the agent and the tool

This is why poorly written tool descriptions are a common, underrated cause of agent misbehavior, a vague or misleading description can lead a perfectly capable model to pick entirely the wrong tool.

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