Convert Python dataclasses, Pydantic models, and TypedDicts into JSON Schema. Then jump directly into the Mock Generator with the schema to craft sample JSON.
Choose which class, dataclass, or BaseModel should be used as the root of the JSON Schema.
Parsing runs entirely in your browser. Your source never leaves the page.
Paste Python classes to produce a JSON Schema instantly.
Supports dataclasses, BaseModel classes, enums, TypedDicts, and modern type-hint syntax.
This online Python class to JSON Schema converter helps you generate JSON Schema from real models (DTOs, structs, and classes) so you can validate payloads, share contracts, and generate mock JSON.
Step 1 – Paste your Python source
Step 2 – Select the Root model (if needed)
Step 3 – Review the JSON Schema output
definitions and $ref when your models reference other models.Step 4 – Validate or generate mock JSON
anyOf/oneOf/allOf), verify the result with a full validator like Ajv.Schema keyword support note
Generated schemas can include $ref and may be edited to include anyOf, oneOf, or allOf. For complex schemas, use a full JSON Schema validator and dereference before mock generation if needed.
Use these tools to validate schemas, generate mock payloads, format JSON examples, and keep code and contracts in sync.
Generate example JSON from your schema for testing, fixtures, and documentation.
Validate JSON against common schema keywords and find mismatches quickly.
Generate dataclasses/Pydantic models from JSON.
Build schema from representative JSON samples to complement model-first schemas.
Explore schema examples and patterns for objects, arrays, enums, and constraints.
Clean up JSON examples before validating against schema or sharing docs.
The converter understands regular classes, dataclasses, Pydantic BaseModel classes, and TypedDict definitions. It reads field annotations, default values, Optional/Union hints, Literal values, and Enum subclasses to build accurate JSON Schemas without uploading your code.
Type hints such as list[str], dict[str, Any], Optional[int], tuple[float, float], and custom classes or enums are mapped to JSON Schema primitives, arrays, objects, or references automatically. When annotations are missing, the tool falls back to simple inference from default values while emitting warnings so you know what to adjust.
Yes. Modern syntax such as PEP 585 generic types, PEP 604 unions (str | None), and decorators like @dataclass(frozen=True) or @validator are supported. It also handles classic typing.Optional/typing.List hints, so code written for Python 3.7+ works out of the box.
After generating the schema, use the "Generate Mock Data" shortcut to open the Mock Generator with the schema preloaded. Configure locales, array counts, and instantly create sample payloads suitable for docs or tests.