JSON Validator

What Is the JSON Validator?

The JSON Validator checks a JSON document for two things: whether it is syntactically correct JSON, and optionally whether it conforms to a JSON Schema. The first check confirms that the document can be parsed by any standards-compliant JSON parser. The second check verifies that the structure, types, and values match a schema you define, which is the foundation of data contract testing between services.

JSON Schema is maintained at json-schema.org and provides a vocabulary for describing the shape of JSON documents. The JSON data model itself is standardised as ECMA-404. Together, these two standards give you everything you need to describe, validate, and document JSON APIs with precision.

How to Use the JSON Validator

1. Paste your JSON document into the left panel.
2. Click Validate Syntax to check whether the document is valid JSON. If it is, you will see a confirmation. If not, the validator highlights the error and shows the line and character position of the problem.
3. For schema validation, paste your JSON Schema into the right panel. The schema should itself be a valid JSON document using the JSON Schema vocabulary.
4. Click Validate Against Schema. The validator checks every field in the document against the schema rules and lists any violations.
5. Each violation shows the path to the failing field, the rule that failed, and a description of what was expected versus what was found.

Technical Background

Syntax validation runs the input through JSON.parse(). Any deviation from the RFC 8259 grammar causes a parse error, and the validator reports the position and a description of what it expected to find. Common errors include trailing commas, unquoted keys, single-quoted strings, and the use of undefined or NaN, which have no JSON representation.

Schema validation uses the JSON Schema specification to apply rules to the parsed document. JSON Schema supports a rich vocabulary of constraints: type checks the data type, required lists mandatory keys, minLength and maxLength constrain string lengths, minimum and maximum constrain numbers, pattern matches strings against a regex, and enum restricts values to a specific list. You can build up arbitrarily complex schemas by combining these with allOf, anyOf, and oneOf.

Given that JSON Schema is itself a JSON document, you can version it, share it, and store it alongside your code. This makes it a practical way to document an API's expected input format and to run automated validation in tests or CI pipelines. With that in mind, the current JSON Schema draft is 2020-12, though earlier drafts like draft-07 remain widely used.

Common Use Cases

• API contract testing: Validate incoming request bodies and outgoing response payloads against a schema to catch mismatches between what a service promises to send and what it actually produces.
• Configuration validation: Application configuration files can be validated against a schema at startup. This catches typos, missing required fields, and type errors before they cause runtime failures.
• CI pipeline checks: Add schema validation as a step in your CI pipeline to prevent broken JSON from reaching production. This is particularly valuable for data files that are edited by hand.
• Debugging: When an API call fails because of a malformed payload, pasting the payload here quickly identifies exactly which field is wrong and why.

For viewing and navigating the JSON you are validating, the JSON Tree Viewer gives you a structural overview. For fixing formatting issues, the JSON Formatter tidies up the document before you validate it.

Limitations to Know

That said, JSON Schema validation only checks the structure and types defined in the schema. It does not perform business logic validation, such as checking whether a date range is chronologically valid or whether two fields are mutually exclusive in a domain-specific way. Those checks need to be implemented in application code.

In practice, the schema validator on this page implements the core JSON Schema vocabulary. Some advanced keywords from the latest drafts may not be fully supported. For production use in server-side code, dedicated libraries like ajv for Node.js or jsonschema for Python give you full draft support and better performance.

Conclusion

The JSON Validator covers both syntax checking and schema-based structural validation in one place. Syntax checking catches parse errors immediately. Schema validation gives you a precise, shareable specification of what valid JSON looks like for your use case and highlights every deviation. For further reading on the schema vocabulary, json-schema.org has comprehensive documentation and worked examples.