LLM Tool‑Use Design
How to structure tool definitions, parse arguments, and handle multi‑step reasoning loops so your agent actually gets things done.
Schema first
Every tool needs a JSON Schema that is strict enough to prevent hallucinated parameters but permissive enough to handle real‑world ambiguity. Use required arrays and narrow enum constraints.
Argument parsing
Never trust raw LLM output. Validate every field before execution. Coerce types aggressively — models often emit strings where integers are expected. Return structured errors so the model can self‑correct on the next turn.
The reasoning loop
A single tool call is rarely enough. Design for iterative refinement: the model calls a tool, inspects the result, and decides whether to call another tool or emit a final answer. Cap the loop at a fixed iteration budget to prevent runaway chains.
Tool descriptions matter
The description string is the model's primary signal for when to invoke a tool. Write it in imperative mood, include example scenarios, and explicitly state what the tool cannot do. A good description prevents both over‑calling and under‑calling.
Observability
Log every tool invocation with its arguments, result, and latency. This trace is invaluable for debugging why an agent took a wrong turn. Structured logs beat printf debugging every time.