JSON Schema Validator

What Is JSON Schema?

JSON Schema is a vocabulary for describing the shape of JSON data. Instead of hoping that an API response, configuration file, or message payload looks the way you expect, you write a schema that states the rules: which properties are required, what type each value must have, the allowed range for numbers, acceptable patterns for strings, and how arrays should be constrained. Schemas are themselves written in JSON, so they travel with your data and can be version controlled like any other file.

This validator checks a JSON document against a schema and reports every violation with its instance path, the keyword that failed, and a plain description of the problem. Validation runs entirely in the browser with the Ajv library, so proprietary API payloads and internal configuration never leave your machine.

What Gets Validated

The validator implements JSON Schema draft-07, the most widely deployed draft, and recognizes the keywords you will meet in everyday schemas:

• Structure: type, properties, required, additionalProperties, minProperties, maxProperties.
• Strings: minLength, maxLength, pattern, and formats such as email, date-time, uri, uuid, and ipv4.
• Numbers: minimum, maximum, exclusiveMinimum, exclusiveMaximum, multipleOf.
• Arrays: items, minItems, maxItems, uniqueItems, contains.
• Values: enum, const.
• Logic: allOf, anyOf, oneOf, not, and conditional if / then / else.

Keywords that the validator does not recognize are ignored rather than treated as errors, so schemas written for other drafts will still validate the parts they have in common.

Reading the Error Report

When validation fails, every problem is listed separately instead of stopping at the first one. Each row contains three pieces of information:

• Instance path points at the exact location in your data using JSON Pointer notation. The root document is /, the second tag in an array is /tags/1, and a nested field is /shipping/city.
• Keyword names the rule that failed, such as required, type, or format.
• Message describes the failure in human terms, for example "must have required property 'email'" or "must be integer".

A document with several mistakes produces several rows, which makes fixing a batch of data faster than correcting one error at a time.

Common Use Cases

• API contracts. Validate sample requests and responses against the schema that documents them, catching drift between the documentation and the implementation.
• Configuration files. Check that deployment configuration, feature flags, or CI settings match the expected structure before anything reaches production.
• Data pipelines. Verify that records produced by an export or transformation job conform to the schema consumers expect.
• Form and code generation. Schemas feed generators for forms, TypeScript types, and validation libraries, and testing the schema first catches structural mistakes early.
• Learning JSON Schema. Watch how each keyword reacts as you change the data, which is faster than reading the specification alone.

How to Use This Validator

1. Paste the JSON document you want to check into the left panel. You can also upload a .json file.
2. Paste the JSON Schema into the second panel. The built-in sample shows a typical object schema with required fields, an enum, an email format, and array constraints.
3. Validation runs instantly as you edit either panel. Fix the schema or the data and the results update in place.
4. Read the error table for paths and messages, then copy or download corrected data as needed.

Tips for Writing Schemas

• Start by adding "required" for the fields that must always exist; most real world failures are missing properties.
• Use "additionalProperties": false to catch typos in key names, but remove it when your consumers forward unknown fields.
• Prefer enum over free strings for fields with a known set of values.
• Add format constraints for emails, dates, and URIs so obviously wrong values fail before they reach application code.
• Use anyOf or oneOf sparingly. Deep composition makes error messages harder to act on.

Privacy

Parsing, compilation, and validation all run locally in your browser. The document and schema are never uploaded or stored, which matters when you are checking internal API contracts or configuration that contains infrastructure details. The tool works offline after the page loads.

Related Tools

Continue your JSON workflow with these tools:

• JSON Formatter
• JSON to JSON Schema Generator
• JSONPath Tester
• JSON Diff
• JSON to Zod