JSON Schema Validator

Paste a JSON Schema and a JSON document to check the data against common schema rules — types, required properties, string patterns, numeric ranges, enum, const, array constraints, and additionalProperties. Validation runs entirely in your browser, so neither your schema nor your data is ever sent to a server.

The JSON Schema project defines a schema as a JSON document whose keywords describe constraints and annotations. Its official guide recommends declaring a dialect with $schema; see Declaring a JSON Schema.

What is JSON Schema?

JSON Schema is a vocabulary for describing the shape of a JSON document — which fields are required, what type each value must be, and the rules each value has to satisfy. Instead of writing manual checks in code, you declare the contract once and let a validator enforce it. It is widely used to validate API request and response bodies, configuration files, and form input before that data reaches your application logic.

How to validate JSON against a schema

1. Paste your JSON Schema into the left panel (or pick a ready-made example from the dropdown).
2. Paste the JSON data you want to check into the right panel.
3. Click Validate — results update automatically as you type.
4. Review each error, which shows the JSON path of the offending value and what rule it violated.

For example, this schema requires a name string and an integer age between 0 and 150:

{
  "type": "object",
  "required": ["name", "age"],
  "properties": {
    "name": { "type": "string", "minLength": 1 },
    "age":  { "type": "integer", "minimum": 0, "maximum": 150 }
  }
}

Read error paths as a repair map

Each failure names the instance path (the value in your JSON), the schema path (the keyword that failed), and a concrete next step. Paths use JSON Pointer notation, consistent with the JSON Schema output model's instanceLocation and schema-location concepts.

JSON Schema drafts at a glance

Drafts are dialects, not feature flags that every validator handles automatically. Keep $schema explicit and confirm your runtime library supports that URI.

When to use schema validation

• API contracts — confirm that a response matches what your client expects before you write parsing code against it.
• Configuration safety — catch a misspelled key or wrong type in a config file long before it crashes a deploy.
• Data pipelines — reject malformed records at the boundary instead of letting bad data spread downstream.
• Documentation — a schema doubles as machine-readable documentation of your data structures.

Frequently asked questions