Key Facts
- Category
- Security & Validation
- Input Types
- textarea, select
- Output Type
- text
- Sample Coverage
- 4
- API Ready
- Yes
Overview
JSON Schema Validator checks your JSON data against a defined schema to ensure structure, data types, and required fields match specifications. It supports multiple JSON Schema drafts including Draft 4, 6, 7, 2019-09, and 2020-12, providing detailed error messages that pinpoint exactly where your data deviates from the expected format.
When to Use
- •Validating API request and response payloads during development to ensure they contain required fields and correct data types
- •Checking configuration files before deployment to catch missing environment variables or type mismatches that could cause runtime failures
- •Verifying data integrity when importing JSON from external sources, third-party APIs, or user-generated content
How It Works
- •Paste the JSON data you want to validate into the JSON Data textarea
- •Enter your validation rules into the JSON Schema field, defining required properties, types, and constraints
- •Select the appropriate Schema Draft version from the dropdown menu (defaults to Draft 7)
- •Execute validation to receive immediate feedback with specific error paths and descriptions for any invalid data
Use Cases
API Development: Ensure incoming request bodies contain mandatory fields like authentication tokens and conform to expected data types before processing
Configuration Management: Validate application settings files to prevent deployment failures caused by missing database URLs or incorrect port number types
Data Pipeline Quality Control: Verify JSON records from external feeds match expected schemas before inserting them into databases or analytics systems
Examples
1. Validate User Registration API Payload
Backend Developer- Background
- Building a REST API endpoint that accepts user registration data with strict requirements for email format and password complexity.
- Problem
- Need to reject malformed requests with clear error messages before they reach the database or authentication logic.
- How to Use
- Paste the incoming request body into JSON Data, define a schema requiring 'email' as string with format 'email' and 'password' with minimum length 8, select Draft 7.
- Example Config
-
{"type":"object","required":["email","password"],"properties":{"email":{"type":"string","format":"email"},"password":{"type":"string","minLength":8}}} - Outcome
- Validates that email contains proper format and password meets length requirements, returning specific errors for missing fields or format violations.
2. Verify Microservice Configuration Files
DevOps Engineer- Background
- Managing service configurations stored as JSON that must contain specific environment variables, port numbers, and database connection strings.
- Problem
- Prevent service outages caused by missing required keys or type errors such as port defined as a string instead of an integer.
- How to Use
- Input the service config JSON and a schema defining 'database_url' as required string and 'port' as integer with minimum value 1024, then validate.
- Outcome
- Catches type mismatches like port '8080' versus 8080 and missing required fields before deployment to production.
3. Validate E-commerce Product Data Import
Data Engineer- Background
- Importing product catalogs from supplier APIs where price must be positive numeric values and SKU must follow specific alphanumeric patterns.
- Problem
- Ensure imported data meets inventory system requirements to prevent pricing errors or products without identifiers entering the database.
- How to Use
- Paste the product JSON array and define schema with price as number with minimum 0, SKU as string with pattern validation, and required fields array.
- Outcome
- Identifies records with negative prices, missing SKUs, or incorrect data types before database insertion, maintaining data quality.
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FAQ
Which JSON Schema draft versions are supported?
The tool supports Draft 4, Draft 6, Draft 7, 2019-09, and 2020-12 specifications. Select the version that matches the schema you are using.
How do I interpret validation error messages?
Error messages specify the exact path to the invalid field, the expected type or constraint, and the actual value received, allowing you to locate and fix issues quickly.
Can I validate complex nested objects and arrays?
Yes, the validator handles deeply nested structures, arrays with item constraints, and references using the $ref keyword according to your selected draft specification.
Is there a size limit for the JSON input?
The tool accepts standard textarea input suitable for most API payloads and configuration files; extremely large datasets should be validated using command-line tools.
Does this tool modify or format my JSON data?
No, the validator only checks your data against the schema and returns validation results without altering, formatting, or reformatting the original input.