Structured Output Validator

A structured output validator checks whether JSON data conforms to a JSON Schema definition. JSON Schema is a vocabulary that lets you describe the structure, types, and constraints of JSON data — specifying required fields, data types (string, number, boolean, array, object), value ranges, string patterns, and more.

Structured output validation is increasingly important in AI/LLM workflows. When you ask an AI model like GPT-4, Claude, or Gemini to return structured JSON (for API responses, data extraction, or function calling), you need to verify that the output actually matches your expected schema. A missing required field or wrong data type can break downstream processing, and catching these errors early is critical.

This validator supports the full JSON Schema specification including type checking, required fields, enum validation, string patterns and formats (email, URI, UUID, date, IPv4), numeric ranges, nested objects, arrays with item schemas, and advanced features like $ref, allOf, anyOf, and oneOf. Paste a schema and an output, and see instant validation results with detailed error paths. Everything runs in your browser — no data is sent to any server.

1. Define or load a schema — Paste your JSON Schema in the left panel, or select a preset (User Profile, Product Listing, API Response, Chat Completion, or Advanced Schema with $ref/oneOf/allOf).
2. Paste the AI output — Enter the JSON data you want to validate in the right panel. This could be an AI model's response, an API payload, or any JSON you want to check.
3. Review validation results — The validator instantly shows whether the output is valid (green checkmark) or has errors (red list). Each error shows the exact JSON path and what went wrong.
4. Fix and re-validate — Edit either the schema or the output and see results update in real time. Error paths like $.user.email pinpoint exactly where the issue is.

• Complete JSON Schema support — Validates type, required, enum, minLength, maxLength, pattern, minimum, maximum, format, and nested object/array schemas.
• Format validation — Checks string formats including email, uri, date, date-time, uuid, ipv4, and ipv6 — catching invalid values that match the type but not the format.
• Advanced schema features — Supports $ref (schema references), allOf (must match all), anyOf (must match at least one), and oneOf (must match exactly one) for complex validation logic.
• Precise error paths — Each validation error includes the exact JSON path (e.g., $.address.zipCode) so you know exactly which field failed and why.
• Preset schemas — Load common schema patterns (User Profile, Product Listing, API Response, Chat Completion, Advanced) to get started quickly or use as templates.
• 100% client-side — Your schemas and data never leave your browser. No server processing, no data storage.

• Validating AI model outputs — Check that GPT-4, Claude, Gemini, or other LLM responses match your expected JSON structure before processing them in your application.
• API contract testing — Validate that API responses conform to their documented JSON Schema, catching regressions before they reach production.
• Schema development — Iteratively develop and test JSON Schemas by validating sample data against your schema as you refine constraints.
• Data pipeline validation — Verify that data transformations produce output matching the expected schema before loading into databases or downstream systems.
• OpenAI function calling — Test that your function call schemas correctly validate the structured outputs returned by AI models in function calling / tool use scenarios.