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Instructor uses tool-calling internally when the runtime operates in OutputMode::Tools, which is the default mode. For most applications, this is an implementation detail — you define a response model and read the result. However, the FunctionCall addon takes this concept further by letting you extract arguments for real PHP functions, methods, or closures directly from natural language. This is particularly useful when building tool-use capabilities for AI chatbots or agents.

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Extracting Arguments for a Function

The FunctionCallFactory inspects a function’s signature via reflection and builds a response model that matches its parameters. The LLM then extracts the correct argument values from the input text. 
use Cognesy\Addons\FunctionCall\FunctionCallFactory;
use Cognesy\Instructor\StructuredOutput;

/** Save user data to storage */
function saveUser(string $name, int $age, string $country) {
    // ...
}

$text = "His name is Jason, he is 28 years old and he lives in Germany.";

$args = (new StructuredOutput)->with(
    messages: $text,
    responseModel: FunctionCallFactory::fromFunctionName('saveUser'),
)->get();

// Call the function with extracted arguments
saveUser(...$args);
// @doctest id="ff65"
The docblock comment on the function is included in the schema sent to the LLM, giving it additional context about what the function does.

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Extracting Arguments for a Method

You can also extract arguments for class methods by specifying both the class and method name. 
use Cognesy\Addons\FunctionCall\FunctionCallFactory;
use Cognesy\Instructor\StructuredOutput;

class DataStore {
    /** Save user data to storage */
    public function saveUser(string $name, int $age, string $country) {
        // ...
    }
}

$text = "His name is Jason, he is 28 years old and he lives in Germany.";

$args = (new StructuredOutput)->with(
    messages: $text,
    responseModel: FunctionCallFactory::fromMethodName(DataStore::class, 'saveUser'),
)->get();

(new DataStore)->saveUser(...$args);
// @doctest id="5e5d"

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Extracting Arguments for a Callable

Closures and other callables work the same way. 
use Cognesy\Addons\FunctionCall\FunctionCallFactory;
use Cognesy\Instructor\StructuredOutput;

/** Save user data to storage */
$callable = function(string $name, int $age, string $country) {
    // ...
};

$text = "His name is Jason, he is 28 years old and he lives in Germany.";

$args = (new StructuredOutput)->with(
    messages: $text,
    responseModel: FunctionCallFactory::fromCallable($callable),
)->get();

$callable(...$args);
// @doctest id="3204"

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How It Works

Under the hood, FunctionCallFactory uses CallableSchemaFactory to reflect on the callable’s parameters and produce a Schema object. That schema is then wrapped in a Structure that Instructor can use as a response model. The LLM receives a JSON Schema derived from the function signature (including parameter names, types, and docblock descriptions) and returns matching values.

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Output Modes

By default, Instructor uses OutputMode::Tools (tool-calling). You only need to change the mode when a specific provider or workflow requires JSON output instead of tool-calling. The function call extraction works with any output mode.