JSON to JSON Schema
Generate a JSON Schema from example JSON — validation rules, inferred.
How to use JSON to JSON Schema
- Paste a representative JSON example — fully populated beats sparse; real API responses work well.
- Read the inferred schema — types and structure transcribed automatically.
- Refine the contract: set required fields, add value constraints, decide additionalProperties.
- Deploy it — API validation middleware, OpenAPI spec, or "$schema" reference for editor tooling.
What is JSON to JSON Schema?
A JSON-to-Schema generator reads an example document and infers the JSON Schema describing it: every field's type (string, number, boolean, array, object), the nesting structure, and the skeleton of a validation contract. Feed it a real API payload and get the schema that would validate payloads shaped like it.
JSON Schema is the standard for saying "data must look like this" — API request/response validation, config file checking, form generation and code generation all run on it. Writing schemas by hand for an existing structure is tedious transcription; inferring from an example does the transcription and leaves you the judgment calls.
About the JSON to JSON Schema
Paste a representative JSON example and get the inferred schema — types, properties and structure mapped, ready to refine.
The refinement that turns inference into a contract: mark which properties are required (inference can't know intent — it saw them present, not mandatory), add constraints the example can't express (minimum/maximum for numbers, minLength/pattern for strings, enum for closed value sets), and decide additionalProperties — whether unknown fields should fail validation. An example shows one instance; you know the rules.
Where the schema then works: validating incoming API requests before they hit business logic, OpenAPI/Swagger definitions (which embed JSON Schema), config validation in CI, and editor autocomplete — VS Code reads schemas to autocomplete and lint JSON files as you type. Use a representative example: a payload with a null or empty array teaches the inferrer less than a fully populated one.