`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

fastmcp.cli

fastmcp.client

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fastmcp.resources

fastmcp.server

fastmcp.tools

fastmcp.utilities

json_schema_type

`fastmcp.utilities.json_schema_type`

Functions

`json_schema_to_type`

Classes

`JSONSchema`

​fastmcp.utilities.json_schema_type

​Functions

​json_schema_to_type

​Classes

​JSONSchema

`fastmcp.utilities.json_schema_type`

Functions

`json_schema_to_type`

Classes

`JSONSchema`