JSON Model Generator

JSON to TypeScript / Java / C# Model Generator

The JSON Model Generator turns raw JSON into strongly typed models for TypeScript, Java, and C#. This saves time when integrating APIs, building SDKs, or scaffolding DTOs for backend services. Instead of writing classes by hand, you can paste JSON and generate interfaces or classes instantly. The tool infers types from the data and produces clean, readable models in your chosen language.

Typed models improve reliability, reduce runtime bugs, and make codebases easier to maintain. They also serve as living documentation for API responses. This generator runs fully in your browser, so your JSON data is not uploaded or stored.

Supported outputs

• TypeScript: Interfaces with proper types and nested structures.
• Java: POJO-style classes with fields.
• C#: Classes with properties and getters/setters.

How to use the model generator

1. Paste JSON in the left editor.
2. Select your target language (TypeScript, Java, or C#).
3. Click Generate Models to produce code on the right.
4. Copy or download the generated models.

Example JSON input

{
  "id": 123,
  "name": "Avi",
  "active": true,
  "roles": ["admin", "editor"],
  "profile": {
    "email": "avi@example.com",
    "score": 9.5
  }
}

Example TypeScript output

export interface Root {
  id: number;
  name: string;
  active: boolean;
  roles: string[];
  profile: Profile;
}

export interface Profile {
  email: string;
  score: number;
}

Example Java output

public class Root {
  public double id;
  public String name;
  public boolean active;
  public List<String> roles;
  public Profile profile;
}

public class Profile {
  public String email;
  public double score;
}

Common errors and solutions

• Invalid JSON: The generator requires valid JSON. Use JSON Validator first if parsing fails.
• Mixed types in arrays: Arrays with different types can produce any or generic types. Normalize the array or separate types.
• Null values: Nulls are treated as any or Object. Provide non-null samples for accurate types.
• Unexpected naming: Class names are inferred from keys. Rename the generated classes as needed.
• Large JSON: Very large inputs can slow generation. Use a smaller sample or subset for modeling.

Best practices for accurate models

• Use representative sample JSON with all expected fields.
• Provide arrays with multiple examples if the type varies.
• Validate JSON before generating models to avoid errors.
• Review and rename classes to match your project conventions.

Typical workflows

Frontend integration: Paste a real API response and generate TypeScript interfaces. This gives you immediate typing for components and data hooks.

Backend scaffolding: Generate Java or C# models to create DTOs quickly, then refine with annotations for serialization or validation.

Documentation: Use generated models as a quick way to document the shape of payloads for teams and clients.

Limitations to be aware of

• Type inference is based on the sample JSON, not on all possible values.
• Null values may produce generic types. Provide concrete examples when possible.
• Field naming is inferred; you should rename classes and properties to fit conventions.
• Complex polymorphic APIs may require manual adjustments after generation.

Model generation checklist

• Use a representative payload with all fields included.
• Validate JSON for syntax errors.
• Generate models and review the output.
• Add annotations or attributes for serialization if needed.
• Commit the models and keep them synced with API changes.

Language-specific tips

TypeScript: Interfaces are lightweight and great for frontend projects. Consider adding readonly for immutable data or converting interfaces to types if needed.

Java: Generated POJOs are a starting point. Add getters/setters, constructors, and Jackson annotations as required.

C#: Properties are created with getters/setters. Add data annotations for validation or JSON attributes for naming conventions.

Extra example: array of objects

Input JSON:

{
  "items": [
    { "id": 1, "label": "A" },
    { "id": 2, "label": "B" }
  ]
}

TypeScript output (simplified):

export interface Root {
  items: Item[];
}

export interface Item {
  id: number;
  label: string;
}

Naming and consistency tips

Generated class names are derived from keys, which may not match your preferred naming conventions. After generation, rename classes to align with your project (e.g., UserProfile instead of Profile). If a JSON key conflicts with a reserved word, rename the property in your model and map it using annotations or serialization attributes.

Keep model definitions in sync with your API. When a payload changes, regenerate models and review diffs. This is a simple way to keep your codebase aligned with real data and prevents subtle runtime errors.

For stricter validation, pair generated models with schema validation in your pipeline. Models define types in code, while schemas enforce structure at runtime. Together they provide a strong contract for reliable data exchange.

This tool is best used at the start of a project or when onboarding new APIs. It gives you a fast baseline that you can refine as requirements evolve.

FAQs

Does this generate full annotations or serialization attributes?
The output focuses on core structure. Add annotations (like Jackson or JsonProperty) manually if needed.

Can I generate models for nested objects?
Yes. Nested objects produce nested interfaces or classes automatically.

Why is everything a number or Object?
The generator infers types from the sample JSON. If values are inconsistent, it may fall back to Object.

Is my JSON uploaded?
No. Everything runs locally in your browser.

Can I use this for API contracts?
Yes. It’s great for scaffolding models from real API responses.

Can I generate one language at a time?
Yes. Choose the language in the toolbar before generating.

Can I generate models for arrays at the root?
Yes, but wrapping the array in an object provides richer class naming.

Does it handle enums?
Enums are inferred only when the data is consistent. You may need to create enums manually afterward.

Can I use this for JSON Schema?
Use the JSON Schema Validator or JSON Schema Builder for schema workflows.

How do I keep models updated over time?
Regenerate models whenever the API changes and compare outputs to spot breaking changes.

Does it support optional fields?
Optionality is inferred based on the sample. Use representative data to capture fields that may be missing.