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AI tool calling is a model’s structured request to use a capability provided by an external system. The model can identify a tool and supply its inputs, but the host application or service must execute the operation and return the result. The model can then use that result to answer the user or request another tool.
What AI tool calling means
A tool is a capability made available to a model—for example, retrieving current information, calculating a value, or asking another system to perform an action. When a model decides a tool is useful, it returns a structured request that identifies the tool and provides inputs or arguments. That request is not the operation itself: the application or service responsible for the tool runs it.
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OpenAI commonly calls this function calling or tool calling; Google uses function calling or tool use; Anthropic uses tool use or function calling. The terms describe a similar general pattern, but the available tools and API mechanics differ by provider. See the official guides from OpenAI, Google, and Anthropic.
The Tool Desk
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In a typical client-side flow, the model proposes a call and application code carries it out. The exchange usually follows these steps:
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- The developer defines the tools. Each definition explains what a tool does and what inputs it accepts. The definitions are sent with the user’s request.
- The model requests a tool. If it determines a tool is useful, it returns a structured call, commonly naming the tool and supplying arguments.
- The host validates and executes it. The application checks the request and runs the corresponding code using its own permissions and integrations.
- The host returns the result. The application sends the tool’s output back to the model, associated with the call.
- The model continues. It may use the result to produce a user-facing answer, or it may request another tool.
This cycle can repeat. Some APIs support multiple calls or hosted tools, and the exact representation of requests and results is provider-specific. OpenAI’s guide describes the client-side call-and-result flow; Google also describes applications executing function calls, while Anthropic documents both client-executed and server-executed tools.
What tool calling does—and does not—guarantee
Tool calling gives a model a way to request access to data, computation, or actions beyond its own generated response. It does not guarantee that the requested operation is valid, safe, authorized, or correct. Those outcomes depend on the tool’s implementation and the surrounding application logic, including how it validates requests and controls permissions.
Structured arguments help software interpret a request, but they are not authorization. OpenAI documents a strict mode that, for supported models and request configurations, can make generated function-call arguments conform to a supplied schema when the schema meets strict-mode requirements and uses the supported JSON Schema subset. Schema conformity says nothing by itself about whether the operation should be allowed or whether its result is accurate. See OpenAI’s explanation of function calling and strict mode.
Why provider terminology and execution details vary
There is no single tool-calling format that applies to every provider. In OpenAI’s terminology, a function is a type of tool defined with a JSON schema; its platform also supports custom tools that can use free-form text and built-in tools. Google’s documentation describes function declarations and application-side execution. Anthropic distinguishes client tools, which the application executes, from server tools, which run on Anthropic’s infrastructure.
When implementing an integration, check the current guide for the chosen provider and model. In particular, verify where execution happens, what tool interface is supported, how the call-and-result flow is managed, and which schema features or strictness options apply. These implementation details can change as APIs evolve.
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