JSON Example
Generated JSON Schema
Schema will appear here...
JSON Schema is a specification for describing the structure, types, and constraints of JSON data (IETF standard). It serves as a "blueprint" or "contract" for JSON data, defining what the data should look like—each field's type, whether it's required, and what values it can hold. JSON Schema itself is a JSON document, so it can be read and processed by any JSON parser.
The core value of JSON Schema lies in data contracts: API providers and consumers can use Schema to agree on data formats, reducing communication overhead and runtime errors. It is widely used in REST API documentation (OpenAPI specification), form validation, configuration validation, and data migration testing.
The generated Schema includes the following keywords:
$schema — Declares the JSON Schema version used
type — Data type (string/number/integer/boolean/array/object/null)
properties — Property definitions for objects
required — List of required field names
items — Type definition for array elements
anyOf — Union type (used when a field has inconsistent types across examples)
enum — Enumerated values (auto-inferred when a field has few unique values)
examples — Example values (optional)
This JSON Schema generator is simple yet powerful. Here is the detailed guide:
Single Example Mode: Paste a JSON object in the input box. The tool analyzes each field's data type and automatically infers and generates the corresponding JSON Schema definition. For example, string fields generate {"type": "string"}, number fields generate {"type": "number"}, and nested objects are recursively parsed into properties structures.
Multi-Example Mode (Recommended): Wrap multiple JSON objects in an array, such as [{"name":"A","age":1},{"name":"B","age":2}]. The tool analyzes all examples, marks fields that appear in every object as required, and generates anyOf union types for fields with inconsistent types. Multi-example mode produces more accurate and stricter Schemas.
Configuration Options: Enable "Infer Required Fields" and the tool will count field occurrence frequency across all examples. Fields appearing in 100% of examples are added to the required array. Enable "Include Examples" to attach an examples array to each field, helping API documentation readers understand field meanings. The Schema Version dropdown lets you choose Draft 4/6/7, with Draft 7 being the most widely supported standard.
View Results: The generated Schema appears in the output area with syntax highlighting. Click "Copy Schema" to copy to clipboard, or "Download JSON" to save as a .json file. If the input JSON is malformed, the tool shows a red error message pointing to the specific location.
JSON Schema is widely used in modern software development. Here are typical scenarios:
Automated API Documentation: In frontend-backend collaboration, API response formats frequently change. Developers can collect real API response samples and use this tool to auto-generate Schema definitions, then embed them into Swagger/OpenAPI documentation. This keeps API docs in sync with actual data structures and reduces integration issues caused by outdated documentation.
Form Data Validation: User input forms in web applications can be validated using JSON Schema. For example, a user registration form can define that the name must be a string of 2–50 characters, age must be an integer between 1 and 150, and email must match a valid format. The Schema generated by this tool can be directly fed into validation libraries like AJV or jsonschema to quickly build form validation logic.
Configuration Validation in CI/CD: In microservice architectures, each service's configuration files (e.g., application.json, settings.json) must conform to expected structures. Including Schema validation in CI pipelines can automatically detect configuration errors before deployment. This tool helps DevOps teams quickly generate validation Schemas from existing configuration samples, ensuring production environment correctness.
Evolution of JSON Schema: JSON Schema is standardized by the IETF and has evolved through multiple versions. Draft 4 (2013) established the basic framework; Draft 6 (2017) introduced const, propertyNames, and other new keywords; Draft 7 (2019) added if/then/else conditional validation and semantic annotations like readOnly/writeOnly. The latest Draft 2019-09 and Draft 2020-12 introduced more powerful recursive references ($recursiveRef) and annotation features. This tool currently generates Draft 7 format for maximum compatibility.
Limitations of Schema Inference: Auto-generated Schemas are "conservative"—they infer rules based on existing data but cannot foresee all future data variants. For example, if the age field is always an integer in examples, the tool infers integer; but if actual data occasionally contains the string "unknown", validation will fail. Therefore, treat the auto-generated Schema as a starting point and supplement with anyOf, default, and other edge-case handling based on business knowledge.
Related Ecosystem Tools: After generating a Schema, you can use it with AJV (the fastest Node.js validation library), jsonschema (Python), gojsonschema (Go), and other libraries for runtime validation. OpenAPI 3.0 directly uses a subset of JSON Schema Draft 5 to describe data models. Postman also supports importing JSON Schemas to automatically generate request body mock data. The Schemas generated by this tool are fully compatible with these tools.
JSON Schema is a specification for describing the structure, types, and constraints of JSON data. It serves as a "blueprint" for JSON, validating whether data matches the expected format. It is widely used for REST API documentation, form validation, data migration, and more.
The tool infers Schema definitions by analyzing data types in JSON examples. For single examples, the result is deterministic. For multiple examples, the tool merges type information to generate a more permissive Schema. We recommend manually refining the generated Schema for edge cases, especially enum values and format constraints.
Yes. The tool recursively parses nested objects and multi-dimensional arrays, automatically inferring type structures at each level. For arrays, it analyzes all elements and infers a unified items type. If element types differ, it generates an anyOf union type.
Currently supports JSON Schema Draft 7, the most widely compatible version. Draft 7 is supported by most modern validation libraries and API frameworks, including AJV, FastAPI, and Spring Boot.
No. All processing happens locally in your browser. Your JSON data never leaves your device. You can safely paste JSON examples containing sensitive information.
Since everything is processed in the browser, the limit mainly depends on available memory. Typically, JSON files up to a few MB can be handled. For very large files, we recommend extracting representative data samples for Schema inference, then manually extending to the full structure.
When multiple JSON examples are provided, the tool analyzes which fields appear in every object. Fields that appear in all examples are marked as required. This helps generate stricter Schema definitions and reduces runtime validation failures.
When a field has few unique values (typically 5 or fewer) and the values are short strings or integers, the tool automatically infers an enum type. For example, a "status" field with only "active" and "inactive" values will generate {"enum": ["active", "inactive"]}.