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Generate JSON Schema from Sample Data

Paste a JSON object and this tool infers a JSON Schema definition from it automatically. It detects types, required fields, nested objects, arrays, enums, and patterns — giving you a schema you can use for validation, documentation, or API contract definitions. Manually writing JSON Schema from scratch is slow and error-prone; this generator handles the boilerplate so you can focus on refining the rules.

How to Use

  1. 1Paste a sample JSON object into the input editor
  2. 2The tool analyzes the structure and generates a JSON Schema
  3. 3Review the generated schema — adjust types, required fields, or constraints as needed
  4. 4Copy or download the schema for use in your project
  5. 5Optionally provide multiple samples to improve type inference accuracy

Key Features

  • Automatic type detection for strings, numbers, booleans, arrays, objects, and null
  • Required field inference based on present keys
  • Nested object and array schema generation
  • Enum detection for arrays with repeated string values
  • JSON Schema draft-07 output
  • Editable output for adding custom constraints
  • Copy and download the generated schema
  • Handles deeply nested and complex JSON structures

Common Use Cases

  • API contract definition

    Generate a schema from a sample API response and use it to validate future responses, catching breaking changes early.

  • Form validation rules

    Create validation schemas for form submissions by generating a schema from expected input, then adding constraints like required fields and patterns.

  • Configuration file validation

    Generate a schema for application config files so that invalid configurations are caught before deployment.

  • Documentation generation

    Use the generated schema as the basis for API documentation, ensuring docs stay aligned with actual data structures.

  • Data pipeline contracts

    Define schemas for data flowing between services or pipeline stages, enabling automated validation at each step.

Examples

  • Simple user object

    Input: {"name": "Alice", "age": 30, "email": "alice@example.com"} generates a schema with string, integer, and string (format: email) types, all marked as required.

  • Nested object with array

    Input: {"team": "Engineering", "members": [{"name": "Bob", "role": "dev"}]} generates nested object schemas with array item definitions.

Understanding the Concepts

The generator analyzes your sample JSON recursively: it determines the type of each value, identifies which fields are present (marking them as required), infers array item types, and detects potential enum values when arrays contain a small set of distinct strings. For nested objects, it generates nested schema definitions. The output follows the JSON Schema draft-07 specification by default and can be adjusted. You can then edit the generated schema to add constraints like minLength, maximum, pattern, or custom validation rules that cannot be inferred from a single sample.

Frequently Asked Questions

Which JSON Schema version does it generate?

The output follows JSON Schema draft-07 by default, which is widely supported by validation libraries in most programming languages.

Can it infer types from multiple samples?

Providing multiple samples improves inference — the tool can detect optional fields (present in some samples but not others) and union types.

Does it detect string formats like dates or emails?

Basic format detection (date-time strings, email patterns, URIs) may be inferred from common patterns, but you should verify and add explicit format annotations for accuracy.

Can I edit the generated schema?

Yes. The output is fully editable. Add minLength, maximum, pattern, or any other JSON Schema keyword to refine the validation rules beyond what can be inferred automatically.

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