agent-tool-builder

LLM agents hallucinate or fail silently during function-calling when tool schemas and descriptions are ambiguous. Defining precise JSON Schema, explicit error formats, and validation rules makes tool behavior clear to the model and prevents these failures.

Designing JSON Schema for LLM agent tools involves creating precise input definitions, adding representative examples, and writing human-readable descriptions. This guides model behavior, reduces token waste, and ensures the agent sends exact parameters.

The best way to format error handling for agent tool calls is defining machine-readable error formats with recovery suggestions. This structured approach allows agents to detect problems and safely retry or fallback during function-calling.

Yes, this tool schema design applies to multi-agent orchestration, API wrappers, and LLM integration. It satisfies requirements for structured inputs and outputs, explicit validation rules, and graceful recovery across various function-calling scenarios.

To prevent agents from looping when API calls fail, provide structured error responses with actionable recovery suggestions. Explicit error formats and validation rules guide the model to gracefully handle rate limits or invalid inputs without repeating failures.