arcade-mcp/arcade/tests/tool/test_create_tool_definition_pydantic.py

from typing import Annotated, Optional, Union

import pytest
from pydantic import BaseModel, Field

from arcade.core.catalog import ToolCatalog
from arcade.core.schema import (
    InputParameter,
    ToolInputs,
    ToolOutput,
    ValueSchema,
)
from arcade.sdk import tool


class ProductOutput(BaseModel):
    product_name: str = Field(..., description="The name of the product")
    price: int = Field(..., description="The price of the product")
    stock_quantity: int = Field(..., description="The stock quantity of the product")


@tool(desc="A function that returns a Pydantic model")
def func_returns_pydantic_model() -> Annotated[ProductOutput, "The product, price, and quantity"]:
    return ProductOutput(
        product_name="Product 1",
        price=100,
        stock_quantity=1000,
    )


@tool(desc="A function that accepts a required Pydantic Field with a description")
def func_takes_pydantic_field_with_description(
    product_name: str = Field(..., description="The name of the product"),
) -> str:
    return product_name


@tool(desc="A function that accepts an Pydantic Field")
def func_takes_pydantic_field_optional(
    product_name: Optional[str] = Field(None, description="The name of the product"),
) -> str:
    return product_name


# Annotated[] takes precedence over Field() properties
@tool(desc="A function that accepts an annotated Pydantic Field")
def func_takes_pydantic_field_annotated_description(
    product_name: Annotated[str, "The name of the product"] = Field(
        ..., description="The name of the product???"
    ),
) -> str:
    return product_name


# Annotated[] takes precedence over Field() properties
@tool(desc="A function that accepts an annotated Pydantic Field")
def func_takes_pydantic_field_annotated_name_and_description(
    product_name: Annotated[str, "ProductName", "The name of the product"] = Field(
        ..., title="The name of the product???"
    ),
) -> str:
    return product_name


@tool(desc="A function that accepts a Pydantic Field with a default value")
def func_takes_pydantic_field_default(
    product_name: str = Field(description="The name of the product", default="Product 1"),
) -> str:
    return product_name


@tool(desc="A function that accepts a Pydantic Field with a default value factory")
def func_takes_pydantic_field_default_factory(
    product_name: str = Field(
        ..., description="The name of the product", default_factory=lambda: "Product 1"
    ),
) -> str:
    return product_name


# TODO: Function that takes a Pydantic model as an argument: break it down into components? Look at OpenAPI, do they represent nested arguments?
# TODO: Should title and default_value be added to JSON schema?
# TODO: Pydantic Field() properties stretch goal: gt, ge, lt, le, multiple_of, range, regex, max_length, min_length, max_items, min_items, unique_items, exclusive_maximum, exclusive_minimum, title?


### A complex, real-world example
class ProductFilter(BaseModel):
    column: str = Field(..., description="The column to filter on")


class FilterRating(ProductFilter):
    greater_than: int = Field(..., description="The rating to filter greater than", gt=0, lt=5)


class FilterPriceGreaterThan(ProductFilter):
    price: int = Field(..., description="The price to filter greater than", gt=0)


class FilterPriceLessThan(ProductFilter):
    price: int = Field(..., description="The price to filter less than", gt=0)


class ProductSearch(BaseModel):
    column: str = Field("Product Name", description="The column to search in")
    query: str = Field(..., description="The query to search for")
    filter_operation: Union[FilterRating, FilterPriceGreaterThan, FilterPriceLessThan] = None


class ProductOutput(BaseModel):
    product_name: str = Field(..., description="The name of the product")
    price: int = Field(..., description="The price of the product")
    stock_quantity: int = Field(..., description="The stock quantity of the product")


@tool
def read_products(
    action: Annotated[ProductSearch, "The search query to perform"],
    cols: list[str] = Field(
        ...,
        description="The columns to return",
        default_factory=lambda: ["Product Name", "Price", "Stock Quantity"],
    ),
) -> Annotated[list[ProductOutput], "Data with the selected columns"]:
    """Used to search through products by name and filter by rating or price."""

    pass


@pytest.mark.parametrize(
    "func_under_test, expected_tool_def_fields",
    [
        pytest.param(
            func_returns_pydantic_model,
            {
                "output": ToolOutput(
                    value_schema=ValueSchema(val_type="json", enum=None),
                    available_modes=["value", "error"],
                    description="The product, price, and quantity",
                )
            },
            id="func_returns_pydantic_model",
        ),
        pytest.param(
            func_takes_pydantic_field_with_description,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="product_name",
                            description="The name of the product",
                            required=True,
                            inferrable=True,
                            value_schema=ValueSchema(val_type="string", enum=None),
                        )
                    ]
                )
            },
            id="func_takes_pydantic_field_with_description",
        ),
        pytest.param(
            func_takes_pydantic_field_optional,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="product_name",
                            description="The name of the product",
                            required=False,
                            inferrable=True,
                            value_schema=ValueSchema(val_type="string", enum=None),
                        )
                    ]
                )
            },
            id="func_takes_pydantic_field_optional",
        ),
        pytest.param(
            func_takes_pydantic_field_annotated_description,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="product_name",
                            description="The name of the product",  # Annotated[] takes precedence over Field() properties
                            required=True,
                            inferrable=True,
                            value_schema=ValueSchema(val_type="string", enum=None),
                        )
                    ]
                )
            },
            id="func_takes_pydantic_field_annotated_description",
        ),
        pytest.param(
            func_takes_pydantic_field_annotated_name_and_description,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="ProductName",
                            description="The name of the product",  # Annotated[] takes precedence over Field() properties
                            required=True,
                            inferrable=True,
                            value_schema=ValueSchema(val_type="string", enum=None),
                        )
                    ]
                )
            },
            id="func_takes_pydantic_field_annotated_name_and_description",
        ),
        pytest.param(
            func_takes_pydantic_field_default,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="product_name",
                            description="The name of the product",
                            required=False,  # Because it has a default value
                            inferrable=True,
                            value_schema=ValueSchema(val_type="string", enum=None),
                        )
                    ]
                ),
            },
            id="func_takes_pydantic_field_default",
        ),
        pytest.param(
            func_takes_pydantic_field_default_factory,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="product_name",
                            description="The name of the product",
                            required=False,  # Because it has a default value factory
                            inferrable=True,
                            value_schema=ValueSchema(val_type="string", enum=None),
                        )
                    ]
                ),
            },
            id="func_takes_pydantic_field_default_factory",
        ),
        pytest.param(
            read_products,
            {
                "inputs": ToolInputs(
                    parameters=[
                        InputParameter(
                            name="action",
                            description="The search query to perform",
                            required=True,
                            inferrable=True,
                            value_schema=ValueSchema(val_type="json", enum=None),
                        ),
                        InputParameter(
                            name="cols",
                            description="The columns to return",
                            required=False,
                            value_schema=ValueSchema(val_type="json", enum=None),
                        ),
                    ]
                ),
                "output": ToolOutput(
                    value_schema=ValueSchema(val_type="json", enum=None),
                    available_modes=["value", "error"],
                    description="Data with the selected columns",
                ),
            },
            id="read_products",
        ),
    ],
)
def test_create_tool_def_from_pydantic(func_under_test, expected_tool_def_fields):
    tool_def = ToolCatalog.create_tool_definition(func_under_test, "1.0")

    assert tool_def.version == "1.0"

    for field, expected_value in expected_tool_def_fields.items():
        assert getattr(tool_def, field) == expected_value