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Schema guide

JSON Schema Validation Explained

A simple explanation of JSON Schema validation for APIs, imports and data pipelines.

Recommended first tool

Start with Schema Checker

Validate JSON or CSV data against a JSON Schema in one universal tool.

Open Schema Checker

What this guide covers

  • Schema validation checks shape and rules
  • Generate first, then refine
  • Validate the right level
  • Use schema validation after syntax validation

Schema validation checks shape and rules

JSON Schema describes what a JSON document is allowed to contain. It can require fields, enforce types, restrict enum values, validate nested objects, describe array item structures, and document which values are optional.

A schema validator compares data against that contract. When validation fails, the useful output is not just raw keyword output. It should tell you which field failed, what rule was expected, and whether the data or the schema is likely the thing that should change.

Generate first, then refine

Generated schemas are a strong starting point when you have representative JSON. They can infer object properties, array item shapes and primitive types quickly, but they only know the examples you provided.

Before using a generated schema as a production contract, review required fields, nullable values, date formats, number ranges and enum values. A field that appears in one sample may not be required in every real record.

Validate the right level

For a single API payload, validate the root object. For a list export, validate the array and its item schema. For CSV rows converted to JSON, decide whether the schema should describe the full array or each row object.

This distinction matters because an array schema and an object schema produce different errors. If every row should be checked independently, the validator should show row-level context so fixes are actionable.

Use schema validation after syntax validation

A schema validator assumes the input JSON parses first. If the JSON is invalid, repair or validate syntax before running schema checks. Once syntax passes, schema validation catches missing fields, wrong types, enum mismatches and nested shape problems.

Use the same approach for CSV: parse the CSV, convert rows into objects, then validate those objects against a JSON Schema that matches the import contract.

Example schema check

Use JSON Schema Generator on `{"email":"ada@example.com","active":true}`. Then validate a payload missing `email` in JSON Schema Validator. The report should identify the required-field failure and suggest adding the missing property.

FAQ

Is JSON Schema only for developers?

No. It is useful anywhere a data file must match a contract, including imports, migrations, no-code tools and internal operations.

Can generated schemas be trusted without review?

No. Generated schemas reflect sample data. Review required fields and edge cases before using them for production validation.

Next workflow

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