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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

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Functions

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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

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Classes

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JSONSchema