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Make tool contracts explicit, validate arguments before execution, keep tool responses focused, and trace failures before claiming cost savings. These controls can stop malformed tool calls from reaching an API, but a valid schema alone cannot ensure that an agent chose the right tool or understood the task correctly.
The practices below are an implementation guide, not a report of a team’s measured results: no baseline, specific intervention, or API-credit savings figure is established for the “we stopped” framing.
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Why agents make bad tool calls
A tool call has two separate parts: the model selects an action and supplies arguments, while your application defines the contract that action must satisfy. That contract should specify tool names, parameter types, required fields, allowed values, and what the tool returns. If the contract is vague—or several tools appear to do the same thing—the model has more room to guess.
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There are also distinct failure types. An argument can be structurally invalid, such as a missing required field; structurally valid but semantically wrong, such as a valid account ID for the wrong customer; or appropriate in form but attached to the wrong tool. Treating all three as “bad JSON” leads to ineffective fixes.
Build and enforce a precise tool contract
Define the API boundary
Start with the underlying API operation, not with a prompt asking the model to behave. For each tool, document its exact purpose, accepted parameters, types, required values, enums or ranges where applicable, and how optional or null values are handled. Make the tool’s output shape clear too, especially which identifiers or status fields a later step may need.
Where supported, enable strict schema enforcement. OpenAI’s function-calling documentation describes strict mode and its schema requirements; it also warns that strict requests using incompatible schemas can be rejected, while requests without strict enabled may take a best-effort non-strict path in some cases. OpenAI function calling documentation
Strict schemas can require fields and rule out undeclared properties, but they do not establish that the selected operation is correct, that a value refers to the intended entity, or that the caller is authorized to perform it. Validate again in application code before side effects, and apply authorization and business-rule checks independently.
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Give each tool one clear, non-overlapping task, a recognizable name, and a concise description of when to call it and what arguments it needs. Anthropic’s engineering guidance puts the test plainly: “When writing tool descriptions and specs, think of how you would describe your tool to a new hire on your team.” It recommends thoughtful tools for high-impact workflows and explicit input and output descriptions. Anthropic engineering: Writing effective tools for AI agents—using AI agents
Do not assume a naming convention will improve selection for every model. The practical test is whether your agent reliably distinguishes tools that perform different operations; evaluate that on representative tasks rather than relying on a naming rule alone.
Reduce retries and unnecessary API usage
Return only what the next decision needs
A tool response becomes more context for the model to process. Return the smallest useful result, and use filtering, pagination, ranges, or truncation when an API can produce large responses. Preserve identifiers only when a downstream call needs them. Anthropic’s guidance specifically recommends high-signal results and bounded responses to avoid making the model sift through irrelevant output.
Make validation failures actionable
When validation rejects a call, report the specific correction rather than a generic failure. For example, identify a missing required field or an invalid enum value and give the accepted values when safe and useful. A targeted error gives the agent a chance to repair the request; a vague “invalid input” encourages blind retries.
Set a retry limit and record why each retry occurred. A schema error, a tool timeout, and an authorization denial are not interchangeable reasons to repeat an operation. Do not automatically retry calls that may already have produced a side effect unless the operation is safe to repeat or you have an idempotency mechanism.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle refusals and incomplete outputs separately
Structured output is not a guarantee that every response will be a successful tool call. OpenAI documents that refusals may not follow the supplied structured-output schema and can be indicated by a refusal field. Check refusal and completion status before treating parsed content as an executable request. OpenAI structured outputs documentation
Keep separate handling paths for a refusal, an incomplete response, a schema rejection, a tool error, and a valid tool call. Do not send a refusal or partial output through the same parser-and-execute path as a complete, validated request.
Trace failures before claiming credits were saved
OpenAI describes tracing and evaluations as ways to inspect agent workflow execution and assess performance. OpenAI: New tools for building agents For your own implementation, record the selected tool, arguments, schema-validation result, tool response, retry count, and model or API usage. Treat this as an engineering recommendation, not evidence that a particular team has already implemented it or achieved savings.
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The March 11, 2025 OpenAI announcement reports SimpleQA accuracy of 90% for GPT-4o search preview and 88% for GPT-4o mini search preview. Those are search-preview benchmark results; they do not measure tool-schema accuracy, failed-call retries, or API-credit savings, so they should not be used to substantiate those outcomes.
Quick Recap
Implementation sequence
- Specify the contract: write down each tool’s purpose, allowed parameters and types, required fields, enums or ranges, optional-value behavior, and response shape.
- Enforce before execution: enable strict schema mode where supported, then validate arguments and authorization in your application before any side effect.
- Clarify the tool set: remove unnecessary overlap and give each remaining tool a distinct name and description that explains when it should be used.
- Bound the response: filter or paginate large results and return only the information needed for the next decision.
- Recover deliberately: return corrective validation feedback, cap retries, and distinguish safe retries from operations that may already have taken effect.
- Measure outcomes: trace calls and compare a defined test set against a baseline, separating malformed arguments, wrong tool selection, semantic errors, refusals, and tool failures.
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