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Instantly convert JSON strings into Kotlin data class code with automatic nested structure handling and type inference. Perfect for Android and backend developers.
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
A JSON-to-Kotlin data class converter reads the structure visible in a sample and drafts corresponding Kotlin types. JSON objects contain named values, and nested objects or arrays add more structure for the generated model to represent. Kotlin data classes are declarations commonly used to hold structured values. The output is a starting point based on the sample you provide, not a complete schema for every response your service might return.
Select the root type name and a serialization style, then inspect the generated properties, nested declarations, and any annotations. The available styles include kotlinx.serialization, Jackson, Klaxon, and plain Kotlin types; the chosen style changes the generated output.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Paste a complete JSON object or array into the input. Make sure quotation marks, commas, and braces or brackets are valid JSON.
Set the root type name to a useful name for the response, then select the serialization style that matches your project. Advanced settings also let you set a package name and choose to generate only type definitions.
Read the output and compare each field with the source JSON. Check nested types, array element types, property names, and nullable types in particular.
Copy the code into your Kotlin project and compile it with the libraries and versions your project uses. Add or adjust field rules to match the API contract, including cases not represented by the sample.
A sample such as {"user":{"id":7,"name":"Mina"},"tags":["reader","writer"]} gives the converter an object containing a nested user and a string array. Confirm the resulting names and types against real response examples before relying on them.
Use cases
See how the tool fits into real work and everyday tasks.
Use a representative API response to create an initial set of declarations, then compare the result with the endpoint documentation and other response variants.
Paste a saved sample to see its nesting expressed as Kotlin types. This can help map the shape before you decide how to handle optional fields or inconsistent values in application code.
Q&A
Find concise answers to common questions and confusing cases.
No. A sample only shows the values and fields present in that sample. A missing key in one example, a nullable value, and a field that is always required are different cases; compare multiple examples or the actual schema and make the requiredness decision yourself.
The generated style determines whether serialization-specific annotations or helpers appear. Choose the style your project uses, then verify the generated code and dependencies against that library’s official documentation.
The converter infers types from observed values, so a null or a field represented by different types across responses may need manual adjustment. Use representative samples and review the generated declaration before compiling or deserializing production data.
Notes
Review scope, result limitations, and important precautions before use.
A generated model cannot infer fields absent from the sample, all possible values, or every API version. Confirm nullability, defaults, naming, and polymorphic cases using the service contract and multiple representative payloads. Selecting a serialization style does not install its library or configure your project; your build must include and configure the matching dependency.
Do not paste secrets or private customer payloads into a web tool unless your organization permits that use. The generated code should be reviewed and compiled in the project where it will run.
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