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Convert JSON data structures into Scala 3 case class code with automatic type inference to boost development efficiency.
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
Paste a JSON value and choose Scala 3 to produce type declarations shaped around the sample, commonly case classes for object data. A case class is a Scala construct for describing structured values. The generated text is code to review and adapt; it is not a running JSON parser, serializer, or connection to a JSON library.
Inference follows what appears in the submitted sample. Object keys become fields, nested objects can become nested types, and values inform field types. JSON itself has no dedicated date value, so a date-looking string is still just a JSON string unless you change the generated model and add parsing logic.
Use valid JSON that resembles the data your program will receive. For example, {"name":"Maya","age":18,"active":true} gives evidence for a string, an integer-like number, and a Boolean. A single record cannot reveal every value that might appear in production.
One sample cannot establish whether a field is optional across records, whether null is allowed, or whether an array may contain other types. An empty array provides no example element to infer. Treat the output as a draft against a sample, then compare it with the API schema or data contract.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Paste a syntactically valid JSON object or array into the input area. Use representative nested values when those structures matter.
Select Scala 3 and set the root type name. Add a package name if your project needs one, and enable the type-only option if you want declarations without extra output.
Review each field name, type, nested declaration, and collection. Check how unusual JSON keys map to Scala identifiers.
Copy or download the result, then compile it in the target project with the project’s Scala version and dependencies.
Try additional examples for missing fields, nulls, empty collections, and values near the limits relevant to your application. Reconcile differences manually with the contract instead of assuming one sample describes every record.
Use cases
See how the tool fits into real work and everyday tasks.
Generate a first draft from a representative response, then compare every field against the endpoint documentation and required or nullable rules.
Use nested declarations to make an example’s shape easier to inspect before deciding how the application should model it.
These workflows produce code scaffolding. They do not select or configure your project’s JSON parsing library.
Q&A
Find concise answers to common questions and confusing cases.
Do not assume so. JSON represents it as a string, and date parsing requires an explicit application-level decision.
No. The result is type declarations. Parsing and serialization require a compatible library and the project’s corresponding configuration.
No. A field present in one object may be absent in another. Use the full schema or multiple representative records to determine optionality and null handling.
Notes
Review scope, result limitations, and important precautions before use.
Generation does not verify the complete data contract, all possible values, or compatibility with a project’s Scala version and dependencies. Review names and edge cases, compile in the destination project, and test serialization behavior with representative data before relying on the model.
Related
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