JSON Data Generator

This page is the canonical guide to generating realistic mock JSON arrays in the browser with DevUtilities. Configure a template above while you read, or jump to a topic below. Synthetic data is assembled locally — schemas and payloads are never uploaded.

JSON Generator builds fictional but realistic JSON arrays for API mocks, UI prototypes, and test fixtures. Entropy comes from crypto.getRandomValues — no faker CDN download and no server round-trip.

What you get

• Starter categories: User Profiles, E-Commerce, FinTech, DevLogs, and Custom Schema
• Record quantity slider (1–100) for array length
• Custom fields with typed generators (UUID, email, timestamps, currency, booleans, and more)
• Regenerate values while keeping the same schema shape
• Copy indented JSON for paste into formatters, CSV export, or mock servers

Use this tool when

• You need mock list endpoints without copying production rows
• QA or CI needs disposable fixtures that look like real records
• You are sketching a schema-ish template before writing OpenAPI or JSON Schema
• You must avoid uploading PII to online fake-data services

Prefer a sibling tool when

• You need to validate or beautify existing JSON → JSON Formatter
• You need JSON Schema inference from one example → Structured Output Generator
• You need OpenAPI lint before SDK generation → OpenAPI Validator

Start from a category that matches your domain, then customize fields.

First-time walkthrough for a custom mock array.

1. Select a Dataset Category — or Custom Schema if you need exact field names.
2. Set Record Quantity (1–100) for the length of the output array.
3. For Custom Schema, Add field rows with non-empty keys and choose a data type per field.
4. Click Generate Dataset (or the sparkle toolbar action) to refresh random values.
5. Copy Output and paste into JSON Formatter, JSON to CSV, or your mock server.
6. Export the JSON into version control when you need a stable CI fixture (generation is non-deterministic).

A frontend team needs a /users list shape for Storybook and local MSW handlers, but production exports contain real emails.

How this tool solves it

1. Pick User Profiles or Custom Schema matching your API field names.
2. Generate 20–50 records with example.com emails and UUIDs.
3. Copy the array into your mock handler or paste through JSON Formatter for indentation.
4. Regenerate when demos need fresh values without changing the schema.

Realistic list payloads for UI work without uploading or storing customer PII.

Integration tests need a checked-in JSON fixture. Online generators either require accounts or produce unstable shapes.

How this tool solves it

1. Define Custom Schema fields that mirror the contract under test.
2. Generate once, copy the array, and commit it as a golden fixture.
3. Optionally pipe through JSON Formatter minify for compact fixture files.
4. Re-generate only when the contract changes — do not rely on a seed (none is exposed).

Disposable, schema-shaped fixtures suitable for CI without a faker dependency in the pipeline.

You are designing a new resource and want sample documents before writing OpenAPI components.schemas.

How this tool solves it

1. Add custom fields for every property you expect clients to send or receive.
2. Generate a small array and review types (string vs number vs boolean).
3. Pipe a single object into Structured Output Generator if you need JSON Schema next.
4. Keep emails and IDs fictional — treat the sample as documentation, not production data.

A schema-ish template you can share in a design doc or ticket before locking the OpenAPI.

Generated values follow strict JSON types and conventional test-data formatting for validators and mock APIs.

Some custom fields are missing from the generated array.

Why it happens

Rows with blank keys are skipped during generation so the output stays valid JSON.

Diagnose

Scan the Custom Schema table for empty key cells or duplicate keys that you intended to rename.

Fixes

1. Give every schema row a non-empty camelCase or snake_case identifier.
2. Remove unused rows with the trash action instead of leaving blank keys.
3. Regenerate after renaming keys so the new property names appear in every object.

Synthetic datasets are assembled in your browser. No records, schema definitions, or generated payloads are uploaded.

• Entropy uses crypto.getRandomValues — no remote faker service.
• Emails use example.com and similar fictional domains — not real people.
• Never insert mock data into production databases without environment guards.
• Optional persistence is localStorage only; clear it after sensitive schema work.

Generated arrays are ready to hand off to the rest of the JSON toolchain.

• JSON Formatter — beautify, minify, or tree-explore the sample
• JSON to CSV — spreadsheet export for stakeholder review
• Structured Output Generator — infer JSON Schema from one object
• OpenAPI Validator / Kiota — after you promote the shape into an OpenAPI document

• Match field names to your real API contract so mocks stay honest.
• Commit a copied fixture when CI needs stability — generation is non-deterministic.
• Keep quantity modest (≤100); wide custom schemas on old devices may feel slow.
• Prefer example.com emails and fake account numbers — never paste real PII into the schema.
• Validate output in JSON Formatter before shipping a fixture to another team.