The type keyword is fundamental to JSON Schema because it specifies the data type that a schema should expect.

JSON Documents can be either basic values (strings, numbers, integers, the boolean constants or null), or ordered or unordered list of key:value pairs. We can use JSON Schema to specify that documents can be any of these. For now we concentrate on the specification for values.

This is a type stub package for the jsonschema package. It can be used by type checkers to check code that uses jsonschema.

There are two numeric types in JSON Schema: integer and number. They share the same validation keywords · JSON has no standard way to represent complex numbers, so there is no way to test for them in JSON Schema

Master the full power of JSON Schema with our reference documentation. From basic data types to advanced techniques like conditional validation and schema composition, you will learn everything about JSON Schema keywords through clear explanations and examples.

November 7, 2023 - TypeScript definitions for json-schema. Latest version: 7.0.15, last published: 2 years ago. Start using @types/json-schema in your project by running `npm i @types/json-schema`. There are 697 other projects in the npm registry using @types/json-schema.

January 5, 2026 - Master JSON Schema data types: string, number, integer, boolean, null, object, and array. Learn validation rules, constraints, and testing in Postman.

So far we have only described how to specify types uniquely. But we could also specify that a certain document may be any number of types, such as string or integer, object or array, etc · The basic declaration of multiple types is through the "type" keyword, where we can now have an array ...

February 9, 2026 - JSON Schema specifies several data types, each with its unique properties and validation requirements. These types are crucial for defining the acceptable data format and values within a JSON document.

Types for defining JSON schemas. Synopsis · class JSONSchema a where · data Schema · = Choice [Schema] | Object [Field] | Map Schema · | Array LengthBound Bool Schema · | Tuple [Schema] | Value LengthBound · | Boolean · | Number Bound · | Constant Value ·

Integration for the wwwision/types package that allows to generate JSON Schema files from PHP code ... class Contact { public function __construct(public string $name, public int $age) {} } $schema = (new JsonSchemaGenerator())->fromClass(Contact::class); $expected = <<<JSON { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "integer" } }, "additionalProperties": false, "required": [ "name", "age" ] } JSON; assert(json_encode($schema, JSON_PRETTY_PRINT) === $expected);

Of course, we wouldn't be using JSON Schema if we wanted to just accept any JSON document. The most common thing to do in a JSON Schema is to restrict to a specific type.

JSON Schema defines the media type "application/schema+json", a JSON-based format for describing the structure of JSON data. JSON Schema asserts what a JSON document must look like, ways to extract information from it, and how to interact with it. The "application/schema-instance+json" media ...

A standardized JSON Schema representation that preserves inferred type information

The Basics Structure Generic keywords String type Number type Integer type Boolean type Null type Object type Array type Formats Default value Conditional subschemas Multiple subschemas

Including the import directive from the above example in the script header loads all the existing types in the JSON schema. In import * from jsonschema!example::schema::Person, the only existing type is the Root type, specified at the root which describes an Object that can have three properties (firstName, lastName, and age).

By default, the result of z.toJSONSchema represents the output type; use "io": "input" to extract the input type instead. const mySchema = z.string().transform(val => val.length).pipe(z.number()); // ZodPipe const jsonSchema = z.toJSONSchema(mySchema); // => { type: "number" } const jsonSchema = z.toJSONSchema(mySchema, { io: "input" }); // => { type: "string" }

>>> validate(instance={"name" : "Eggs", "price" : 34.99}, schema=schema) >>> validate( ... instance={"name" : "Eggs", "price" : "Invalid"}, schema=schema, ... ) Traceback (most recent call last): ... ValidationError: 'Invalid' is not of type 'number' It can also be used from the command line by installing check-jsonschema.

{ "$schema": "http://json-schema.org/draft-04/schema#", "title": "Product", "description": "A product from Acme's catalog", "type": "object", "properties": { "id": { "description": "The unique identifier for a product", "type": "integer" }, "name": { "description": "Name of the product", "type": "string" }, "price": { "type": "number", "minimum": 0, "exclusiveMinimum": true } }, "required": ["id", "name", "price"] }