Product JSON / JSONL Schema Validator
Free product JSON schema validator that runs entirely in your browser. Validate JSON and JSONL product feeds against a schema, line by line. No upload.
This product JSON schema validator checks JSON and JSONL product feeds against a JSON Schema, then reports every structural and field-level error line by line. It is built for API-first teams shipping catalog payloads where one malformed record can fail a whole import.
Product JSON / JSONL Schema Validator
The interactive version of this tool is coming soon. It will run entirely in your browser — no login, no upload limits.
Planned tool: product json schema validator
Need this now? Talk to ClaroWhat it checks
The validator parses your input as either a single JSON document, a JSON array of products, or newline-delimited JSON (JSONL) where each line is one record. For every record it reports:
- Syntax errors — unclosed brackets, trailing commas, unescaped characters, and the exact line and column where parsing broke.
- Schema violations — fields that fail the JSON Schema you supply or one of the built-in product presets (required keys, wrong types, out-of-range numbers, pattern mismatches).
- Missing required fields — records lacking mandatory attributes such as
sku,gtin,title,price, oruom. - Type mismatches — a price sent as a string (
"19.99"), a quantity as a boolean, or a nested object where the schema expects an array. - Enum and format failures — currency codes, unit-of-measure codes, or category values that fall outside the allowed set, plus malformed URLs and dates.
- Duplicate and inconsistent keys — repeated identifiers across a JSONL stream, or the same attribute typed differently between records (a common symptom of schema drift).
- Summary counts — total records, valid records, and a grouped list of the most frequent error types so you can fix the highest-impact issues first.
How the JSON schema validator works
The tool builds on the JSON Schema standard — the same vocabulary most product APIs already document their payloads with. You paste or upload a schema (or pick a starter preset), then paste or upload your product data. The validator compiles the schema and runs every record through it, collecting each failure with a JSON Pointer path so you can map an error back to the exact key, even deep inside a nested attribute object.
- 1Provide a schemaPaste a JSON Schema, upload a
.jsonschema file, or select a built-in product preset. - 2Add your dataPaste a JSON object, a JSON array, or a JSONL file where each line is one product record.
- 3Read the resultsSee a per-record pass/fail list, the exact path of each violation, and a grouped error summary.
All processing happens client-side in your browser. Your schema and your product data are never uploaded to a server, which means there is no file-size limit beyond your own machine’s memory and nothing leaves your network. That makes it safe to validate confidential price lists or pre-release SKUs without a vendor agreement.
Related resources
Glossary
What Is Schema Drift?
Why product feeds silently change shape over time, and how it breaks downstream imports.
Glossary
What Is Schema Mapping?
How to align an incoming supplier schema with your canonical product model.
Tool
CSV Encoding & Delimiter Fixer
Clean up delimiter and encoding problems before converting a feed to JSON.
Playbook
Detect and Fix Catalog Data Drift
An API-first workflow for catching drift before it reaches production.
Guide
7 Field-Level Errors That Break Catalog Launches
The recurring data mistakes that fail imports across industries.
Claro
Validate product data at scale
See how Claro validates, deduplicates, and writes back canonical product records continuously.
FAQ
What is the difference between JSON and JSONL for product feeds?
A JSON file holds one document — usually an array of product objects wrapped in [ ... ]. JSONL (newline-delimited JSON) puts one complete product object on each line with no wrapping array. JSONL streams better for very large catalogs because a parser can process and validate one record at a time without loading the whole file into memory. This validator accepts both and reports the line number of any failing JSONL record.
How do I validate a product feed against a JSON Schema?
Supply the JSON Schema that defines your expected structure — required fields, types, allowed values — then run your feed through it. The validator compiles the schema and checks each record, returning the JSON Pointer path of every violation. If you do not have a schema yet, start from a product preset and tighten the required fields and enums to match your own catalog model.
Does my product data get uploaded anywhere?
No. The validator runs entirely in your browser using client-side JavaScript. Your schema and your records are parsed locally and never sent to a server, so you can validate confidential pricing, unreleased SKUs, or partner data without exposure.
Why does a valid JSON record still fail my downstream import?
Passing JSON Schema validation only proves the record is well-formed and matches the declared shape. Your downstream system may enforce business rules the schema does not — a valid GTIN check digit, a unit code that matches the product family, or a category that exists in your taxonomy. Use this tool as the structural gate, then layer identifier and value checks on top.
Can it handle a large product catalog?
Yes, within your browser’s available memory. Because there is no upload and no server-side limit, the practical ceiling is your own machine. For multi-million-record catalogs, JSONL is the better input format because records are validated one line at a time.