> ## Documentation Index > Fetch the complete documentation index at: https://gofastmcp.com/llms.txt > Use this file to discover all available pages before exploring further. # 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 ## Unsupported regex patterns Pydantic uses a Rust-based regex engine that does not support all regex features found in real-world JSON Schemas (particularly those from AWS, Azure, and other large OpenAPI providers). Unsupported constructs include lookahead/lookbehind assertions (`(?!...)`, `(?<=...)`), Unicode property escapes (`\p{Graph}`, `\p{Print}`), and very large compiled patterns. When a `pattern` constraint cannot be compiled, `json_schema_to_type` degrades gracefully: 1. The pattern is **dropped** from the Pydantic `StringConstraints` so the type will not raise a `SchemaError`. 2. A `UserWarning` is emitted with the unsupported pattern. 3. The original pattern is preserved in the type metadata as `x-unsupported-pattern` (visible via `TypeAdapter(T).json_schema()`). 4. Other constraints (`minLength`, `maxLength`) are still enforced. Example: ```python theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}} 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 theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}} json_schema_to_type(schema: Mapping[str, Any] | bool, 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. Boolean schemas are also accepted (`True` = any type, `False` = unsatisfiable). * `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 theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}} 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 theme={"theme":{"light":"snazzy-light","dark":"dark-plus"}} 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`