In a custom-tool setup, an AI model usually does not make the outside API request itself. The application tells the model which tools it can use, the model returns a structured request naming a tool and its arguments, and the application decides whether to run it. The application sends the result back so the model can continue. Some providers also offer tools executed in provider-managed environments, so where a tool runs depends on the tool and setup.
How does AI tool calling work?
Think of the model as a receptionist with a directory and a request form. It can choose a listed capability and fill out the form, but in a custom-tool setup your program carries out the work and decides what is allowed. The model proposes a call; the runtime executes it.
- The application declares tools. A declaration typically provides a tool name, a description, and an input schema. For example,
get_order_statusmight accept anorder_id. These declarations tell the model what it may request and what argument shape is expected. See the OpenAI function-calling guide and Anthropic tool-use overview. - The application sends the user’s request and tool declarations to the model. The model considers whether a listed tool is useful. Tool calling is not a model discovering arbitrary APIs on its own.
- The model returns text or a structured tool request. A request identifies a tool and supplies arguments, such as a location for a weather lookup. Each provider represents this request in its own response format; it is not necessarily a ready-to-send request to an unrelated REST API.
- The runtime checks and executes the request. Your application can validate arguments, check permissions, and call its own code or an external API. Keep API credentials and business logic in the application environment, rather than treating model output as a secure place for them.
- The application sends the result back. The result may be text or structured data. It must be associated with the tool request it answers so the model can interpret it correctly.
- The model continues. It can turn the result into a user-facing answer or request another tool. The application may need to repeat the exchange until the task is complete.
For example, if the user asks for the weather in Paris and the application has declared a get_weather tool with a location argument, the model might return a request equivalent to get_weather(location="Paris"). The application performs the lookup and returns the weather data; the model can then base its answer on that result.
What does “the AI calls an API” actually mean?
It is often shorthand for two different operations. The model sends a tool-call request through the model provider’s API. In a custom-tool flow, your program interprets that request and makes the separate API call or runs the relevant function. A tool request is not evidence that the outside operation happened or succeeded.
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Your runtime remains responsible for operational details such as authentication, permissions, timeouts, errors, retries, and interpreting the response. OpenAI captures the handoff succinctly: “When the model calls a function, you must execute it and return the result.” The same broad request–execution–result pattern appears in the Gemini function-calling documentation and Anthropic’s tool-use documentation.
When the provider runs the tool
Not every tool runs in your application. Providers may offer built-in or server-side tools that execute in provider-managed infrastructure. Google distinguishes built-in tools from custom function calls, while Anthropic distinguishes server tools from client tools. For a particular implementation, check the documentation for that tool to determine where execution happens and who controls it.
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What a tool schema can—and cannot—guarantee
A schema describes the expected input fields and value types, often using JSON Schema. It can help the model produce a well-shaped request. OpenAI’s strict Structured Outputs option can constrain supported function-call arguments to the declared schema when the model and request configuration support it. The provider’s function-calling documentation describes the relevant configuration and limitations.
Correct shape is not the same as a safe or authorized action. OpenAI notes that JSON mode guarantees valid JSON, not that the output matches a particular schema; schema conformance requires Structured Outputs or validation by your application. Even arguments that conform to a schema need application checks for access rights, allowed values, rate limits, and the policy for the requested action.
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OpenAI commonly uses “function calling” or “tool calling”; Anthropic often says “tool use.” The shared idea is a model requesting a capability, but names, response objects, argument fields, identifiers, control settings, and execution arrangements are provider-specific. Do not assume a request object or implementation can be transferred unchanged between providers.
| What to compare | Why it matters |
|---|---|
| Execution location | A tool may run in your application, in a provider-managed environment, or through a combination of both. This affects where code runs and who operates it. |
| Validation and approval | In an application-side flow, your code controls whether a requested operation runs. Actions with significant consequences may need explicit confirmation. |
| Round trips and orchestration | Check how the provider represents requests and results, including repeated or parallel calls, and what follow-up message your application must send. |
| Argument guarantees | Schema-constrained arguments depend on the provider’s capabilities and the chosen model and request configuration; they do not replace authorization checks. |
| Response format | Tool names, argument fields, result objects, and call identifiers vary. Use the provider’s current implementation documentation. |
Why the tool boundary is a security boundary
A tool may expose private information or change something outside the chat, such as sending a message, updating a record, or making a purchase. Treat each tool as a grant of authority, not merely as a convenience.
- Give a tool only the permissions needed for its job.
- Validate every argument in application code, including the user’s right to access the requested data or perform the action.
- Require appropriate human confirmation before consequential or hard-to-reverse actions.
- Treat tool results as data to assess, not as automatically trusted instructions. OpenAI warns that untrusted text returned by a tool can try to steer the model into unintended actions, and recommends trusted tools and confirmation for actions such as sending email, posting online, or purchasing. See its function-calling and safety guidance.
What to check before implementing a tool
Provider APIs and supported model configurations change. The official documentation cited here was available on October 4, 2026; Google’s Gemini tools page was last updated August 18, 2026 UTC. Before building against a provider, check its current documentation for the chosen model, tool type, request and result formats, argument constraints, and execution location.
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