json_schema_type

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:

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

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:

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:

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