JSON Schema Validator

Validate JSON, LLM outputs, and tool inputs against a JSON Schema

JSON Data

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JSON Schema

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Enforce a JSON contract, not just JSON syntax

Syntax validation tells you the document parses. Schema validation tells you it actually has the fields, types, and constraints your code expects. That matters most when JSON is crossing a boundary — between services, between a model and your code, or between a partner system and yours — where a missing field becomes a production incident.

Use the validator when you need to

Validate API request and response bodies

Check that incoming and outgoing payloads match the contract before they reach business logic.

Validate AI structured outputs

Confirm that model responses, tool calls, or agent JSON match the shape your downstream code requires.

Run schema checks in CI on fixtures

Fail builds when sample payloads or fixtures drift away from the documented contract.

How to validate JSON against a schema

  1. 1

    Paste your JSON document into the data editor.

  2. 2

    Paste your JSON Schema (Draft 2020-12) into the schema editor.

  3. 3

    Click Validate and review the path-by-path errors if validation fails.

Common schema-validation workflows

Lock down an API contract

Reject any request body that doesn't match the schema before it touches downstream services.

Verify a model output

Validate structured AI responses against the schema your handler expects.

Catch fixture drift in CI

Run validation on test fixtures so changes to the contract surface immediately.

Related Tools

Frequently Asked Questions

Paste your JSON data in the left editor and your JSON Schema in the right editor, then click Validate. The tool checks every constraint in the schema and reports all validation errors with their exact paths.

The validator supports all JSON Schema Draft 2020-12 features including type checking, required fields, pattern matching, minimum/maximum constraints, array item validation, allOf/anyOf/oneOf combinators, and $ref references.

Each error shows the data path (where the error is), the schema path (which rule failed), and a human-readable message explaining what went wrong. For example: /users/0/email must match format "email".

Yes. If you have an OpenAPI/Swagger spec, extract the relevant schema definition, paste it in the Schema editor, then paste a sample request body in the JSON editor to verify it meets all requirements.

Yes. Paste the model output on one side and the schema you expect on the other. This works well for OpenAI Structured Outputs, Anthropic tool inputs, MCP tool schemas, and any agent workflow where downstream code depends on a strict JSON shape.