tools / developer
Generador de modelos JSON
Convierte un objeto JSON en modelos tipados para TypeScript, Python, Go, Rust, Kotlin, Swift o C#.
ENTRADA JSON
TYPESCRIPT RESULTADO
interface Root {
id: number;
name: string;
email: string;
age: number;
active: boolean;
score: number;
tags: string[];
address: Address;
}
interface Address {
city: string;
zip: string;
}ACERCA DE ESTA HERRAMIENTA
Paste any JSON object or array and instantly generate strongly-typed model definitions in TypeScript, Python, Go, Rust, Kotlin, Swift, or C#, inferring field types from the actual values present - strings, numbers, booleans, nested objects, and arrays all map to their closest equivalent construct in the target language, such as interfaces in TypeScript, dataclasses in Python, or structs in Go, Rust, and Swift. This saves the tedious, error-prone work of hand-writing model definitions when integrating with an API, and is especially useful when you have a real JSON response but no formal schema or documentation to work from.
CÓMO USARLO
- Paste a sample JSON object or array into the input box.
- Select the target language from the available options.
- Generate the model and review the field names and inferred types.
- Check that nested objects and arrays produced their own separate types as expected.
- Copy the generated code into your project and adjust field names, optionality, or types to match cases the sample JSON didn't cover.
CASOS DE USO COMUNES
- A developer integrating a third-party API who has an example response but no official schema, and needs a TypeScript interface to work against.
- A backend developer converting a JSON config file into a Python dataclass so it can be loaded with type checking.
- A mobile developer generating Swift or Kotlin model structs from a REST API response for an iOS or Android app.
- A Go developer converting a JSON payload into a struct with the correct field tags for unmarshalling.
- Someone prototyping quickly who wants a starting point for a C# or Rust type definition instead of writing field-by-field boilerplate.
CONSEJOS Y ERRORES COMUNES
- Type inference is based only on the sample JSON provided, so if a field is null, missing, or an empty array in your sample, the inferred type will likely be too generic - test with a sample that includes realistic, populated values.
- A field that's a number in every one of your sample objects but is sometimes a string in the real API, a common inconsistency in loosely-typed backends, won't be caught unless your sample includes an instance of it.
- Nested objects generate their own named types - review the generated names, since auto-generated names based on the parent field aren't always what you'd choose for a real codebase.
- Optional versus required fields can be hard to infer from a single sample - if you know certain fields are sometimes absent from the API response, mark them optional manually after generation.
MÁS PREGUNTAS
- How does the tool decide if a field should be optional or nullable?
- It generally infers this from whether a field's value is null or missing in the sample JSON provided - if every field is present and non-null in your sample, all fields will typically generate as required, so pasting a representative sample matters.
- What happens with arrays that mix types, like a list containing both strings and numbers?
- Since most target languages expect a single element type per array or an explicit union, mixed-type arrays typically resolve to the broadest common type the language supports, such as a union type in TypeScript - this is one of the trickier edge cases to generate cleanly.
- Does the generated code include validation logic or just type definitions?
- Just structural type definitions - things like value range checks or required-field validation at runtime aren't generated and need to be added separately using your language's own validation libraries.
- Why might a generated Go or Rust struct need manual tag adjustments?
- Languages like Go and Rust rely on tags or attributes to map JSON field names to idiomatic language-cased struct fields, and while the tool generates these, you may want to adjust naming conventions or add custom serialization behavior for your specific use case.