How to Fix Bad Tool Arguments

Bad tool arguments are a common issue in large language model (LLM) applications, often resulting from unclear tool contracts, loose schemas, hidden required contexts, or inadequate validation paths. These problems typically manifest as failed API calls, silent operations, corrupted states, or incorrect user-facing answers. To address these issues, it's crucial to treat tool arguments as production interfaces by validating, versioning, and testing them against real cases while tracing every failure. Common mistakes include vague tool descriptions, overly broad tools, hidden required contexts, and silently accepted invalid JSON. Solutions involve clarifying tool descriptions, narrowing tool scope, making context explicit, rejecting invalid payloads, and implementing a repair loop for recoverable errors. A stricter schema with specific requirements for fields like customer_id, priority, description, and due_date can reduce ambiguity and improve the quality of tool arguments. Additionally, versioning prompt and schema changes and expanding evaluation sets with real production failures can help identify and fix recurring issues, ensuring more reliable and accurate tool interactions.