API Mock Data Generator: From One Example to Many
Testing a UI or writing integration tests against an API that doesn't exist yet (or isn't reachable) usually means hand-writing fake data — tedious, and easy to make unrealistic. This tool takes a single example JSON object and generates as many new, structurally-identical records as you need, with field-name-aware fake values rather than generic placeholders.
A field named 'email' gets a fake email address; a field named 'price' gets a plausible decimal amount — not just a random string or number.
Why Structure Preservation Matters
Random JSON generators that don't look at your actual data often produce a completely different shape than what your code expects — missing keys, wrong array depths, mismatched types. By walking your example's exact structure, every generated record has precisely the same keys, nesting, and array shapes as the original.
Field-Name Heuristics
A generic mock generator would fill a `price` field with any random number, potentially a value like 84719 — not remotely plausible for a currency amount. This tool checks the field name against common patterns (email, name, date, price, id, status, and more) so generated values look like something you'd actually see in a real API response.
Practical Examples
Generating Test Fixtures
Populating a test database with realistic user records.
- 1.Paste one real (or hand-written) example user object
- 2.Set Records to 10
- 3.Copy the generated array as test fixture data
What's Preserved
- Object keys and nesting structure
- Array presence and approximate length
- Original scalar type (string/number/boolean) per field
Good Use Cases
- Populating a frontend with realistic data before a backend exists
- Generating test fixtures for automated tests
- Filling a staging/demo environment with plausible sample data
- Stress-testing UI components with varied data shapes
Frequently Asked Questions
How does it know what kind of fake value to generate?
It walks your example JSON's structure recursively — preserving object keys and array lengths — and for each leaf value, generates a replacement guided by both its original type (string/number/boolean) and its field name: keys containing 'email', 'name', 'date', 'price', 'id', and similar patterns get realistic values matching that field's apparent purpose, not just a random string or number.
Does array length stay the same in generated records?
Each generated record's arrays are sized based on your example array's length (capped at 5 items to keep output reasonable), using the first item's shape as the template for every generated element.
Can I generate multiple records at once?
Yes — use the Records slider (1–20) to generate an array of independently randomized mock objects matching your example's shape.
Why would I use this instead of a schema-based mock generator?
Starting from a real example is often faster than writing a formal JSON Schema — if you already have one sample API response, this tool can immediately produce more variations of it without any schema-authoring step.
Is my example JSON sent anywhere?
No, generation happens entirely in your browser.