Sometimes the best tool call is no tool call. An AI agent should use an external tool when it needs information or an action beyond what it can reliably do with the context it already has—and when the right tool is available and the request is clear and safe enough to proceed. Otherwise, it may be better to answer directly, ask a clarifying question, or stop.
What does it mean for an AI to know when not to use a tool?
It means judging whether an external action is warranted before making it, not merely producing a valid function call. A model can know how to format a request to a browser, database, or other tool yet still call one when it is unnecessary—or fail to call one when the task depends on information it does not have.
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One proposed standard is epistemic necessity: whether the task can be completed reliably using the agent’s internal reasoning over its current context, without external interaction. Hongru Wang and coauthors define the idea in their 2026 ICML position paper, “Agents Should Invoke External Tools ONLY When Epistemically Necessary”. It is a proposed principle, not a universal rule adopted across AI systems.
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In practice, a sound decision considers three questions:
- Is outside information or action necessary? If the answer can be given reliably from the conversation and available context, a tool call may add little.
- Is a suitable tool available? If not, the agent should not imply it can complete the task through a tool it does not have.
- Is the request clear and safe enough to act on? Missing details, conflicting instructions, or a newly apparent risk may call for a question or a pause.
Why successful tool calls do not prove good judgment
Tool execution and tool-use judgment are different abilities. A function-call accuracy test can show whether a model correctly formats or executes a call when prompted. It does not necessarily show whether the model recognized that it should call, ask a follow-up, or admit that the available tools cannot answer.
The 2025 NAACL paper introducing When2Call evaluates those decisions directly: when to generate a tool call, when to ask a follow-up question, and when to recognize that the provided tools cannot answer. Its authors report that state-of-the-art tool-calling models still have significant room to improve, but the abstract does not give one general-purpose reliability percentage.
When should an agent pause, ask, or abstain?
Abstaining does not always mean refusing immediately. Depending on what is missing or what has changed, a better response may be to clarify, explain a limitation, or stop an action already underway.
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- The instruction is ambiguous or constraints conflict: clarify which interpretation or requirement takes priority.
- The available tool cannot do the job: state that limitation instead of pretending a call will resolve it.
- The action is high-stakes or risk becomes apparent: pause rather than proceed automatically.
- A tool fails or its output conflicts with other evidence: do not treat the failed call or disputed result as a dependable answer.
This is also why the decision may need to be revisited during execution. A request can appear safe and feasible at first, then become unsuitable after a tool returns unexpected information.
What do the benchmarks test?
Different evaluations examine different parts of tool judgment. Their results should be read as evidence about the named benchmark and setup, not as a single score for AI systems in general.
| Benchmark or work | Decision examined | Setup and reported finding |
|---|---|---|
| When2Call (2025) | Whether to call, ask a follow-up, or say the available tools cannot answer. | Its authors report room for improvement among state-of-the-art tool-calling models; the abstract gives no single general-purpose percentage. |
| MetaTool (ICLR 2024) | Awareness of whether to use tools and selection among them. | Its ToolE queries cover single- and multi-tool scenarios and four selection subtasks, including similar choices and reliability issues. Eight LLMs were evaluated; the authors report that most struggled with effective tool selection. |
| AgentAbstain (2026) | Whether to act or abstain across multiple reasons to stop, including problems that emerge during execution. | The project reports 263 paired tasks in 42 executable environments, eight abstention scenarios, and 17 frontier models. Its best reported paired accuracy was 59.5%. |
AgentAbstain’s 59.5% is the best result reported among those 17 models on that benchmark’s paired-accuracy measure. Paired accuracy requires getting both sides of a task right: acting when appropriate and abstaining when appropriate. It is not a general accuracy rate for AI tools in everyday use.
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For additional context, the MetaTool proceedings abstract is available from the ICLR 2024 proceedings. These evaluations make tool decisions visible in ways that ordinary call-execution metrics may miss, but they do not establish how often deployed agents misuse tools in real-world settings.
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Yes. Deciding that a tool is needed does not settle which tool to use. Microsoft Research’s BiasBusters page describes preferences for particular providers and for tools listed earlier in the prompt context. It presents filtering choices to relevant tools and then sampling uniformly as a mitigation. The page labels the work ICLR 2026 research and also carries a September 2025 date.
This selection bias is a separate issue from unnecessary calls: an agent might correctly decide to seek outside help but still choose among available tools unevenly. Evaluating the initial decision and the subsequent selection can therefore reveal different weaknesses.
What benchmark scores can—and cannot—tell you
The cited studies show that researchers can test whether an agent calls, asks, selects, or abstains appropriately, and that performance remains imperfect in the evaluations described. They do not support a population-wide estimate of unnecessary tool calls or a universal ranking of all AI systems. A benchmark result belongs to its tasks, models, and scoring method; it should not be generalized into a claim about every deployed agent.
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