How Dummy JSON API Generator Works

You describe the shape of your response using a compact, line-based schema where each line defines a field name and a data type. The tool then creates the requested number of records, formats them as JSON, and returns a copy-ready payload you can paste into your code, mock server, or documentation.

Step-by-step workflow

• 1) Choose response style: pick an array, an object wrapper, or a JSON API-style structure with data and meta.
• 2) Set record count: generate a small sample (for UI work) or a larger batch (for testing pagination and edge cases).
• 3) Optionally set a seed: use a numeric seed to reproduce the same output across runs, which is helpful for snapshots and deterministic tests.
• 4) Define your schema: write one field per line using field:type, for example email:email or status:enum(active,inactive,pending).
• 5) Generate and copy: click Generate to produce a formatted JSON response, then copy or download it as a file.

Key Features

Schema-driven output

Instead of clicking through dozens of toggles, you can define the response contract as text. The same schema can be shared with teammates, stored in a repo, or pasted into tickets so everyone generates identical mock data.

Multiple response modes

Create a plain JSON array for simple lists, wrap results under a resource key for endpoints like /users, or generate a JSON API-style envelope with data and meta for more realistic integrations.

Deterministic generation with seeds

When a seed is provided, the generator produces the same sequence of values every time. That makes UI screenshots consistent, reduces flaky tests, and helps you compare changes to formatting without fighting random diffs.

Practical field types

Use common API field types such as integers, UUIDs, names, emails, usernames, booleans, floats, dates, datetimes, enums, and arrays. Enums let you mimic real-world statuses and lifecycle states.

Copy and download tools

One click copies your output to the clipboard, and the download button saves it as a .json file. This keeps your workflow fast whether you are pasting into a mock server or committing a fixture to a test suite.

Use Cases

• Front-end prototyping: populate tables, cards, and dashboards with realistic payloads before the backend is ready.
• API documentation: generate consistent example responses for OpenAPI/Swagger, README files, or internal wiki pages.
• Automated testing: create fixtures for unit tests, integration tests, and snapshot tests with stable, seed-based output.
• Mock servers: paste generated JSON into tools like local mock endpoints to unblock mobile or web clients.
• Pagination and edge cases: produce larger datasets to validate sorting, filtering, and boundary handling in UI components.
• Demos and sales enablement: show realistic product flows with believable data without exposing customer information.

Because the schema is plain text, you can keep multiple presets (users, products, tickets, logs) and switch between them instantly to fit the scenario you are working on.

Optimization Tips

Model your real contracts

Copy field names directly from your actual API responses or database columns. Keeping names and types aligned reduces refactors later and makes it easier to swap mocked data with real responses.

Use enums for realism

Enums are an easy way to simulate state machines like draft, published, and archived. This helps you test conditional UI and backend validation rules early.

Keep seeds in test code

If you rely on a specific output for tests, store the seed alongside the schema in your test helpers. You will get reproducible results without committing huge fixture files.

FAQ