What does the JSON to TypeScript, Java, Python, C#, Go, Rust, Swift, Kotlin, Dart, and JSON Schema do?
Use this tool when you need a browser utility that infers starter data-model declarations from a representative JSON sample. It accepts a valid JSON object or array and returns source declarations for nine languages or a draft JSON Schema. Results are deterministic for the same input except where cryptographic randomness, current time, or a live provider response is part of the task.
Generated models are a typed starting point
Use output to accelerate scaffolding, then reconcile it with the authoritative schema, nullability rules, naming conventions, and serialization library.
How do you use it?
- Paste representative JSON and choose the target language or JSON Schema.
- Enter a root type name and generate the model.
- Review inferred nullability and collection types before copying the starter code.
Worked example
Infer a TypeScript interface
Input
{"id":1,"name":"Ada","active":true}Output
export interface Root {
id: number;
name: string;
active: boolean;
}Each sample property becomes a field whose type is inferred from its value.
Common errors and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| A field should be optional but is required. | One sample object cannot prove that a property may be absent. | Review the API contract and mark optional fields manually. |
| A numeric type is too broad or too narrow. | JSON has one number syntax and does not declare integer width or precision. | Choose a target type using the real value range and serialization contract. |
Important behavior of Data Model Generator
A sample cannot reveal every optional field, union, integer width, validation rule, or API compatibility constraint.
Standards and implementation references
- RFC 8259 — JSON (opens in a new tab)Defines the source data types available to inference.
- JSON Schema 2020-12 (opens in a new tab)Defines the schema vocabulary used by the JSON Schema target.
Frequently asked questions about Data Model Generator
Does the Data Model Generator upload my input?
No. Processing runs in the current browser tab, and the page does not send tool input to a KitLumi processing endpoint.
What input does the Data Model Generator accept?
It accepts a valid JSON object or array. The workspace checks the input and reports malformed or unsupported values before it produces a result.
What does the Data Model Generator produce?
It produces source declarations for nine languages or a draft JSON Schema. Copy and download controls appear when the output format supports them.
What limitation should I know?
A sample cannot reveal every optional field, union, integer width, validation rule, or API compatibility constraint.
What changed?
Added Ten output targets, Nested type inference, Array element inference, Copyable starter models, responsive controls, explicit runtime disclosure, and task-specific examples and limitations.