Tool Use Patterns

Quick Guide: Tool use (function calling) lets LLMs invoke external functions. The universal pattern is: define tool schemas (JSON Schema for parameters) -> send tools + message to LLM -> detect tool_use in response -> execute locally -> return result to LLM -> repeat until the model responds with text. Guard every loop with a max-step limit, validate all tool inputs before execution, and return structured errors so the model can recover. Use tool choice control (auto, required, none, specific tool) to steer model behavior.

<critical_requirements>

CRITICAL: Before Using This Skill

(You MUST guard every tool loop with a maximum step limit -- unbounded loops risk infinite API calls and runaway costs)

(You MUST validate all tool input arguments before execution -- LLM-generated arguments are untrusted input)

(You MUST return structured error messages to the model when tool execution fails -- never silently swallow errors or return empty results)

(You MUST use JSON Schema for tool parameter definitions -- all major providers require this format)

(You MUST treat tool definitions as token cost -- every tool schema is sent on every API call, so keep descriptions concise but precise)