# fastmcp.utilities.json_schema_type

Convert JSON Schema to Python types with validation. The `json_schema_to_type` function converts a JSON Schema into a Python type that can be used for validation with Pydantic. It supports:

- Basic types (string, number, integer, boolean, null)
- Complex types (arrays, objects)
- Format constraints (date-time, email, uri)
- Numeric constraints (minimum, maximum, multipleOf)
- String constraints (minLength, maxLength, pattern)
- Array constraints (minItems, maxItems, uniqueItems)
- Object properties with defaults
- References and recursive schemas
- Enums and constants
- Union types

## Example:

```python
schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string", "minLength": 1},
        "age": {"type": "integer", "minimum": 0},
        "email": {"type": "string", "format": "email"}
    },
    "required": ["name", "age"]
}

# Name is optional and will be inferred from schema's "title" property if not provided
Person = json_schema_to_type(schema)
# Creates a validated dataclass with name, age, and optional email fields
```

## Functions

### json_schema_to_type

```python
json_schema_to_type(schema: Mapping[str, Any], name: str | None = None) -> type
```

Convert JSON schema to appropriate Python type with validation. **Args:**

- `schema`: A JSON Schema dictionary defining the type structure and validation rules
- `name`: Optional name for object schemas. Only allowed when schema type is “object”. If not provided for objects, name will be inferred from schema’s “title” property or default to “Root”.

**Returns:**

- A Python type (typically a dataclass for objects) with Pydantic validation

**Raises:**

- `ValueError`: If a name is provided for a non-object schema

**Examples:** Create a dataclass from an object schema:

```python
schema = {
    "type": "object",
    "title": "Person",
    "properties": {
        "name": {"type": "string", "minLength": 1},
        "age": {"type": "integer", "minimum": 0},
        "email": {"type": "string", "format": "email"}
    },
    "required": ["name", "age"]
}

Person = json_schema_to_type(schema)
# Creates a dataclass with name, age, and optional email fields:
# @dataclass
# class Person:
#     name: str
#     age: int
#     email: str | None = None
```

`Person(name="John", age=30)` Create a scalar type with constraints:

```python
schema = {
    "type": "string",
    "minLength": 3,
    "pattern": "^[A-Z][a-z]+$"
}

NameType = json_schema_to_type(schema)
# Creates Annotated[str, StringConstraints(min_length=3, pattern="^[A-Z][a-z]+$")]

@dataclass
class Name:
    name: NameType
```

## Classes

### JSONSchema

[json_schema](/content/python-sdk/fastmcp-utilities-json_schema/index.html)  
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