JSON Schema Generator

What is JSON Schema?

JSON Schema is a vocabulary that allows you to annotate and validate JSON documents. It provides a contract for what JSON data is required for a given application and how it should be structured. JSON Schema is used for data validation, documentation, and code generation.

Why Use JSON Schema?

Data Validation: Ensure incoming data meets your requirements before processing. Validate API requests, form submissions, and configuration files automatically.

Documentation: Schemas serve as living documentation for your data structures in a machine-readable format.

Code Generation: Generate TypeScript interfaces, validation functions, and mock data from schemas for consistency.

API Contracts: Define clear contracts between API producers and consumers to prevent integration issues.

Core Schema Types

Primitive Types: string, number, integer, boolean, and null are the basic building blocks with their own validation rules.

Object Type: Represents a JSON object with defined properties, required fields, and additional property rules.

Array Type: Represents arrays with item type definitions, size constraints, and uniqueness requirements.

String Formats: Specify common formats like email, uri, date-time, ipv4, ipv6 for specialized validation.

Schema Generation Process

Our generator analyzes your sample JSON through these steps: Type Detection identifies JavaScript types, Format Recognition detects email/URL/date patterns, Structure Analysis maps nested objects and arrays, Requirement Inference marks present properties as required, and Array Handling determines consistent item schemas.

Common Use Cases

API Development: Generate schemas for request/response bodies and use them in OpenAPI specifications.

Form Validation: Create schemas for form data validation in web applications.

Configuration Validation: Validate application configuration files to catch errors early.

Database Migration: Document database schema changes and compare versions.

Testing: Generate mock data based on schemas for comprehensive testing coverage.

Best Practices

• Use representative sample data that includes all possible fields and edge cases
• Review and adjust required properties - not all sample fields may be required
• Add descriptions and titles to your schema for better documentation
• Use specific formats (email, uri, date-time) for better validation
• Set appropriate minimum and maximum values for numbers
• Add examples to your schema for documentation purposes
• Version your schemas as your data model evolves
• Test schemas against various valid and invalid data samples
• Consider using definitions for reusable schema components
• Use additionalProperties: false to strictly validate object shapes