Tool Overview

This tool checks that a JSON document obeys a JSON Schema. You give it a schema and a JSON instance. It reports whether the data is valid and, if not, lists each problem with a path, a plain-language message, and a short fix suggestion.

APIs and apps often expect JSON in a fixed shape. Wrong types, missing fields, or invalid values cause runtime errors or bad data. Checking data against a schema before use catches these problems early. This tool runs that check in the browser and explains each error so you can fix the JSON quickly.

It is for developers, testers, and anyone who works with JSON and schemas. You need basic JSON and schema concepts. No account or sign-in is required. A related operation involves validating XML schemas as part of a similar workflow.

Background and How JSON Schema Works

JSON is a text format for numbers, strings, arrays, and objects. A JSON Schema describes the allowed shape of such data: which properties exist, their types, minimum and maximum values, patterns, and more. Validating JSON against a schema means checking that every rule in the schema is satisfied by the data.

Schemas are used in APIs, config files, and data pipelines. They make contracts clear and catch mistakes before data is stored or sent. Doing validation by hand is slow and error-prone. A validator runs all rules and reports every failure with a location in the data. For adjacent tasks, validating JSON syntax addresses a complementary step.

This tool uses a standard schema validator. It supports types (string, number, integer, boolean, array, object), formats (for example email, date-time, hostname), required properties, min and max length or value, patterns, enums, array length and uniqueness, and disallowing extra properties. When validation fails, each error is turned into a short message and a suggestion so you know what to change.

Key Features

• Two inputs: One area for the JSON Schema and one for the JSON data. Both must be filled for validation to run. You can paste or upload a file for each.
• File upload: You can load the schema and the data from files. Accepted types are JSON and plain text. Each file is limited to 200KB and 10,000 lines. Larger or longer content is rejected with an error.
• Automatic validation: When you change the schema or the data, the tool runs validation after a short delay. You see pass or fail and the list of errors without clicking a button.
• Schema and data parsing: Both inputs must be valid JSON. If either fails to parse, you get a single error: invalid JSON syntax, with a suggestion to check quotes, commas, and structure.
• Full schema validation: The tool runs all schema rules. It checks types, required properties, formats, minimum and maximum, pattern, enum, array length and unique items, and extra properties. Every failing rule produces an error.
• Error list: Each error shows a plain-language message, the path in the data where the problem is, and a short suggestion on how to fix it. The list appears in a sidebar with a count of issues.
• Beautify: Each editor has a Beautify button. It reformats the JSON with indentation so the structure is easier to read. It does not change the meaning of the data.
• Examples: You can load preset pairs of schema and data (for example User Profile, API Response, Product Catalog, Config File). Each example fills both panels so you can see valid and invalid cases and try changes.
• Clear all: A single action clears both schema and data and resets the result.
• Explain: For each error you can click Explain. The tool sends the schema, data, and that error to a backend service and shows a longer fix suggestion. This feature may be unavailable sometimes. Use the built-in message and suggestion when Explain is not available.

Common Use Cases

Developers use it to check API request or response bodies against a schema before integrating or deploying. Testers use it to confirm that sample JSON passes or fails as expected and to see exact error messages. Anyone maintaining config or data in JSON can paste a snippet and a schema to find missing fields, wrong types, or invalid values. When you write or change a schema, you can paste sample data to see which rules fail and where. When working with related formats, validating XML structure can be a useful part of the process.

How to Use This Tool

1. Open the tool. You will see two panels: one for JSON Schema and one for JSON Data. A results area sits beside them.
2. Enter or paste your JSON Schema in the schema panel. You can also click upload and choose a JSON or text file to load as the schema. Keep the content under 200KB and 10,000 lines.
3. Enter or paste your JSON data in the data panel. You can upload a file for the data as well. The same size and line limits apply.
4. Validation runs automatically when both schema and data are non-empty. Look at the results area. You will see either Validation Passed or Validation Failed. If you have not entered both, the tool asks you to add schema and data.
5. If validation failed, read the first error. The message explains what is wrong in plain language. The path shows where in the data the problem is (for example a property name or an array index). The suggestion tells you how to fix it (for example add a field, change the type, or pick an allowed value). Fix the JSON in the data panel and wait for validation to run again.
6. Use Beautify on either panel to indent the JSON. Use the example buttons to load sample schema and data pairs.
7. For more help on an error, click Explain on that error. Wait for the extra suggestion to appear. If it does not appear, use the built-in message and suggestion to fix the error. Use Clear All to empty both panels and start over.

Reference: Error Types You May See

Tips, Limitations and Best Practices

Keep both schema and data under 200KB and 10,000 lines so the tool can parse and validate them. If you see a size or line error, shorten the content or validate a smaller part.

Both inputs must be valid JSON. Use double quotes for strings, commas between array and object members, and no trailing commas. The tool reports invalid JSON syntax for either panel so you can fix parse errors first. In some workflows, validating YAML syntax is a relevant follow-up operation.

The schema itself must be valid JSON and a valid JSON Schema. If the schema is invalid, compilation may fail and you will get an error. Fix the schema before relying on validation results.

Explain sends the schema, data, and error to a backend service. It may be slow or unavailable. Always use the built-in path, message, and suggestion to fix errors when possible. For related processing needs, validating Kubernetes YAML handles a complementary task.

The tool does not fetch schemas or data from the internet. It only validates what you paste or upload. For production or sensitive data, use your own validation pipeline and security rules.