---
title: json_schema_type
sidebarTitle: 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
    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` <sup><a href="https://github.com/PrefectHQ/fastmcp/blob/main/fastmcp_slim/fastmcp/utilities/json_schema_type.py#L164" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
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
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` <sup><a href="https://github.com/PrefectHQ/fastmcp/blob/main/fastmcp_slim/fastmcp/utilities/json_schema_type.py#L131" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>
