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Path-based errors
Find the exact property or array item that failed instead of receiving only a generic verdict.
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JSON Schema Validator
Validate JSON data against a practical JSON Schema subset and receive errors with exact data paths for types, required fields, arrays, strings and numbers.
Validate JSON data against a separate schema Accept combined data and schema input Report every error with a JSON path Check types, required properties and array items
Private browser processing. Your input is processed on this device and is not sent to the Trexmi server.
INPUT JSON data and schema *
Paste the source value for this specific transformation. Example: {"data":{"name":"Trexmi"},"schema":{"type":"object","required":["name"]}}
Reading file…
Schema validation
Schema validation ready to review and copy.
About the tool
What JSON Schema Validator does
JSON Schema Validator checks JSON data against explicit structural rules. It can identify a missing required property, an unexpected type, a disallowed extra field, an array item that fails its rule, or a string or number outside the permitted range.
The result contains a valid status, an error count and a list of messages with paths such as $.customer.email or $.items[2].quantity. This is more useful than a simple pass/fail message when debugging API payloads, webhook events, configuration files and test fixtures.
Trexmi implements a documented practical subset of JSON Schema. It is not a complete Draft 2020-12 implementation, so unsupported keywords must be checked with the validator used by your production application.
How to use
- Add the data. Paste a complete valid JSON value in the main source field.
- Add the schema. Paste a JSON Schema object in the schema field. You may alternatively submit one object with
dataandschemaproperties. - Run validation. Invalid JSON or an invalid supported schema definition is rejected before data validation.
- Read the paths. Each error points to the affected property or array index.
- Retest. Correct the source and run the same schema again until the result is valid.
Built for the task
Why use JSON Schema Validator?
Focused controls, predictable output, and a workflow designed around this exact transformation.
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Nested validation
Apply property and item schemas recursively through objects and arrays.
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Schema sanity checks
Reject malformed supported keywords such as invalid types, duplicate required names and impossible ranges.
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Machine-readable result
Copy or download a structured JSON validation report.
Useful answers
Questions about JSON Schema Validator
Practical details about input, output, privacy, limits, and the best way to use this tool.
01 Which JSON Schema rules are supported?
The validator supports type, enum, required, properties, items, additionalProperties, minLength, maxLength, pattern, minimum and maximum, including nested schemas.
02 Is every Draft 2020-12 keyword supported?
No. Keywords such as $ref, combinators, conditionals, formats, unevaluated properties and many annotation keywords are not fully implemented here.
03 Can the schema allow more than one type?
Yes. The supported type value can be a string or an array of unique type names.
04 Does it validate JSON syntax first?
Yes. Both the data and schema must be valid JSON before schema validation begins.
05 What is the combined input format?
You can submit {"data":...,"schema":...} in the main field instead of using a separate schema field.
06 Does format validate email or date-time strings?
No. The current validator does not implement format assertions. Use your production JSON Schema library for those checks.
07 Can it block additional object properties?
Yes. Set additionalProperties to false, or provide a schema object to validate undeclared properties.
08 Why should I retest in my application?
JSON Schema implementations can support different drafts, vocabularies and format rules. Your production validator is the final compatibility check.
Learn JSON
Read the complete JSON Guide
Learn JSON objects, arrays, value types, validation, formatting, parsing, API workflows, and common syntax errors.
- Objects, arrays, and values
- Validation and formatting
- API and parsing workflows
Read guide
Practical explanations and examples
Examples
Require an integer ID
A numeric-looking string is still a JSON string and fails an integer rule.
Input
Data: {"id":"42"}\nSchema: {"type":"object","required":["id"],"properties":{"id":{"type":"integer"}}}
Output
$.id — Expected type integer.
Reject unexpected fields
Use additionalProperties false when only declared properties are permitted.
Input
Data: {"name":"Ada","role":"admin"}\nSchema: {"type":"object","properties":{"name":{"type":"string"}},"additionalProperties":false}
Output
$.role — Additional property is not allowed.
What this JSON Schema Validator checks
The supported type names are object, array, string, number, integer, boolean and null. Object schemas can require properties, define rules for named properties and control additional properties. Array schemas can apply one item schema to every element. Strings can use length and regular-expression rules; numbers can use minimum and maximum boundaries.
Understanding validation paths
A path begins at $. Dotted segments identify object properties and bracketed numbers identify array indexes. If $.items[1].sku fails, inspect the sku property in the second array item. Missing required fields appear at the path where the property should exist.
Schema definition errors
The schema itself is checked before it is applied. Supported type names must be valid, required-property names must be unique strings, property and item rules must be schema objects, and minimum values cannot exceed maximum values. The first schema-definition problem is returned separately from data-validation errors.
Important implementation limits
This tool intentionally does not claim full JSON Schema conformance. It does not resolve remote references or implement every applicator, conditional, annotation or format vocabulary. Use the official JSON Schema Draft 2020-12 core specification and the documentation for your production library when those features matter.
Prepare JSON before schema validation
If the source cannot be parsed, check it first with JSON Validator or make it readable with JSON Formatter. When the schema belongs to a complete API contract, continue with OpenAPI Validator. For schemas embedded in tool definitions, continue with MCP Schema Validator or JSON to OpenAI Function Schema.
Privacy and production use
Remove credentials and personal data from real payloads before using an online utility. A valid result confirms the supported rules supplied in this run; it does not prove business correctness, API authorization or compatibility with an unsupported schema keyword.