What is a mock data generator?
A mock data generator creates realistic, synthetic datasets for testing and development. Instead of using real user data, you define a schema of fields—like names, emails, prices, or UUIDs—and the tool generates matching records.
This is essential for developers and QA engineers who need to populate staging databases, build UI components with Storybook, or write automated tests without compromising privacy or relying on external APIs.
Why generate mock data locally?
Traditional mock data generators send your custom schema to a backend server, which can expose internal naming conventions or table structures. Tool127 processes everything locally in your browser.
This means your custom field names, enum values, and generated data never leave your machine. The local approach also enables offline use and eliminates network latency when generating large sets of records.
Supported output formats
JSON: An array of formatted objects, ideal for API mocking.
JSON Lines (JSONL): One object per line without commas, perfect for streaming and bulk database imports.
CSV: Standard comma-separated values compatible with Excel and data analysis tools.
SQL INSERTs: Ready-to-run insert statements for populating relational databases.
TypeScript: A typed interface alongside a constant array, perfect for frontend UI fixtures.
Markdown: A GitHub-flavored markdown table for quick copy-pasting into documentation or tickets.
Supported field types
The schema builder supports over 30 data types across several categories. Identity fields include UUID v4, auto-incrementing IDs, and booleans. Person data covers realistic names, usernames, and emails.
Location and Work fields provide street addresses, cities, companies, and job titles. You can also generate numerical values like integers, floats, prices, or test credit card numbers.
For complex needs, you can define custom enum values (e.g., specific statuses), generate text with lorem ipsum words or paragraphs, and output web formats like URLs, IPv4 addresses, and hex colors.
Seeded mock data for reproducible tests
A random seed controls the generator's pseudo-random number generator (PRNG). This guarantees deterministic output: using the same seed with the same schema will always produce the exact same dataset.
Seeded data is incredibly useful for writing reliable unit tests or generating stable screenshots. If a specific mock value triggers a bug, you can copy the seed and schema to share a reproducible test case with your team.
Use cases
- UI Component Testing: Create stable fixtures for Storybook and frontend tests.
- API Mocking: Generate responses for missing or incomplete backend endpoints.
- Staging Databases: Populate development environments with realistic, safe records.
- Dashboard Demos: Build sales demos with believable names, dates, and prices.
- Documentation: Generate sample JSON or Markdown tables for API docs.
- Pagination Testing: Quickly generate up to 1,000 rows to test list rendering and scrolling.
Privacy-First Mock Data
Tool127 generates all mock data locally in your browser. Your schemas, custom enum options, seed values, and the generated datasets are not uploaded to our servers.
If you enable optional analytics, only basic interactions (like 'Generate clicked' or 'Format changed') are recorded. The actual content of your schema and data is strictly excluded from all analytics events.
Frequently Asked Questions
1. What is a mock data generator?
It is a tool that creates fake but realistic-looking data (names, emails, dates, addresses) to be used in software development and testing.
2. Can I generate JSON mock data?
Yes, JSON is the default export format and is perfect for mocking API responses.
3. Can I export mock data as CSV?
Yes, you can generate comma-separated values (CSV) for use in Excel or data import tools.
4. Can I generate SQL INSERT statements?
Yes, you can define a table name and export standard SQL INSERT statements for your database.
5. Can I generate TypeScript mock data?
Yes, you can export the data as a TypeScript constant array, ready to be pasted into your code.
6. What does seeded mock data mean?
Using the same seed value guarantees that the same random data is generated every time. This is useful for predictable test suites.
7. Is my schema uploaded?
No, all schema configuration and data generation happen locally in your browser.
8. Can I import JSON and create a schema from it?
Yes, you can paste an existing JSON object, and the tool will automatically infer a schema for you.