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Convert JSON data into Crystal class definitions and automatically generate type-safe serialization code.
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
Paste a JSON value and the generator produces Crystal code shaped around the sample. JSON objects hold named values, arrays hold ordered values, and values can be strings, numbers, booleans, null, objects, or arrays. The generated definitions give you a starting model for data such as an API response or configuration file.
Crystal’s standard library includes JSON parsing and serialization support, including JSON::Serializable for user-defined types. Generated code is a draft to inspect in your project; it does not establish the complete contract of a remote API, add business validation, or prove that your project’s Crystal version accepts every option.
Generation follows the values and nesting that appear in the input. A sample containing {"name":"Ari","active":true} shows a string and a Boolean field. It does not reveal whether a later response may omit active, return null, or add fields. A sample with a nested object or array gives the generator more structure to model, but one example still may not cover all valid variants.
JSON permits arrays with different value types. Empty arrays provide no element example. These cases deserve special review because a single inferred element type may not represent every production record.
The output is Crystal type definition code, with serialization-oriented support where configured. The root type name is editable, and advanced settings can change generation choices. Field names, nested types, and serialization behavior should be compared with the JSON keys your application receives. Add or adjust field mapping when the Crystal property name needs to differ from the source key.
The converter does not create HTTP requests, endpoint clients, validation rules, or application methods. It focuses on converting sample structure into code.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Include the outer object or array and enough nested values to expose the shapes you expect to handle. Remove credentials and personal data from examples.
Enter a Crystal type name that fits your project. The page is already set to Crystal output; other language pages are separate tools.
Review nested definitions, property names, array element types, and any serialization annotations. Invalid JSON produces a parse error; check quotation marks, commas, braces, brackets, and escapes.
Copy or download the output, then compile it with the Crystal version and dependencies used by your application. Test it against representative JSON records.
For instance, an object with a text name, numeric age, and Boolean active can guide a basic type definition. Add samples that demonstrate optional, null, or alternate values before relying on the model.
Use cases
See how the tool fits into real work and everyday tasks.
Use a sanitized response example to draft the Crystal data types, then compare the result with the provider’s schema and multiple response versions.
Convert a representative JSON configuration into a type outline, then add project-specific defaults and validation deliberately.
Compare nested JSON with the generated Crystal structure and consult the language documentation to understand parsing and serialization behavior.
Q&A
Find concise answers to common questions and confusing cases.
No. A sample only shows the values it contains. Include examples with nulls and omitted fields where relevant, then review the generated types against the actual data contract.
Check that strings and object keys use double quotes, commas separate members, brackets and braces match, and embedded quotation marks are escaped. The input must be valid JSON text.
Treat it as a starting point. Compile it against your project’s Crystal version and test field mapping, nullability, numeric types, and serialization with real contract examples.
Notes
Review scope, result limitations, and important precautions before use.
Inferred types do not guarantee coverage of future API changes, missing fields, mixed arrays, precision requirements, or business rules. Compare output with an authoritative schema and test data from expected versions. Special field names or serialization behavior may need manual mapping. Remove secrets and personal information from sample JSON before pasting it into any online tool.
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