The Tool Desk
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AI tool calling is a model’s structured request for an external capability—such as looking up data, calculating a value, or initiating an action. The application or service that provides the tool executes the request; the model’s request alone does not perform the operation.
How AI tool calling works
A common tool-calling interaction is a request-and-result loop between a model and the application around it:
- The application defines available tools. The developer describes each tool’s purpose and the inputs it accepts, then makes those definitions available to the model.
- The model requests a tool. If it determines a tool is useful, it returns a structured request identifying the tool and providing arguments.
- The application validates and executes the request. The host application runs its own code, using the permissions and systems available to it.
- The application returns the result. It sends the tool’s output back to the model in the conversation, associated with the relevant call.
- The model responds or calls another tool. It can use the result to answer the user, or request another operation. The cycle can repeat.
For example, if a user asks for the current status of an order, a model might request an order-lookup tool and supply an order identifier. The application—not the model—must validate that request and query the relevant system. The model can then use the returned status in its answer.
That is the typical client-side flow. Some APIs also support multiple calls or tools hosted by the provider, and providers represent requests and results differently. See the OpenAI function-calling guide, Google’s Gemini function-calling documentation, or Anthropic’s tool-use documentation for the implementation details of a specific platform.
What counts as a tool?
A tool is a capability made available to a model. It may retrieve information, perform a calculation, or request an action through an external system. A function is one kind of tool; some platforms also provide built-in tools and other custom tool interfaces.
The model selects and requests a capability using the information it has been given. What that capability actually does depends on the tool’s implementation. A declared tool is not automatically connected to a working service, and a request does not itself grant access to one.
Tool calling is not the same as executing an action
A tool call is a model-generated request, not proof that the requested operation ran. In a client-side setup, application code has to receive the request, decide whether it is valid, execute it, and return the result. That separation gives the host application control over what systems a model can reach and what it may do.
Tool calling can extend a model’s access to external data, computation, and actions, but it does not guarantee a correct or appropriate outcome. That depends on factors including the tool’s implementation, the inputs, and the application’s validation and permission logic.
What structured arguments and strict mode do—and do not do
Tool definitions can describe the shape of expected inputs, helping the application interpret a model’s request. OpenAI documents a strict mode that, for supported models and request configurations, can ensure generated function-call arguments adhere to the supplied schema when it meets strict-mode requirements and uses the supported JSON Schema subset. See the OpenAI Help Center’s function-calling explanation for those limits.
Schema adherence addresses the format of the arguments. It does not establish that the requested operation is safe, authorized, factually correct, or appropriate to execute. The application still needs to apply its own validation and permission checks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why provider terminology and mechanics differ
“Tool calling,” “function calling,” and “tool use” describe closely related capabilities, but the terms and implementation details are not universal. OpenAI describes functions as a type of tool defined with a JSON schema, while also supporting custom tools and built-in tools. Google documents function calling with the Gemini API. Anthropic uses “tool use” and distinguishes tools executed by the application from server tools executed on Anthropic’s infrastructure.
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