MockHero is a synthetic test data API that generates realistic, relational fake data for developers. It supports 156 field types, 22 locales, and outputs JSON, CSV, or SQL.

How do I generate test data with MockHero?

Send a POST request to /api/v1/generate with a JSON schema describing your tables and fields. You can also use plain English prompts or pre-built templates for common schemas like ecommerce, blog, or SaaS.

What field types does MockHero support?

MockHero supports 156+ field types including uuid, email, full_name, phone, address, company, credit_card, iban, ip_address, url, date, timestamp, price, latitude, longitude, color, avatar, paragraph, and many more.

Does MockHero support relational data?

Yes. Use ref fields to create foreign key relationships between tables. MockHero generates referentially consistent data across all related tables.

Can I use MockHero with AI coding agents?

Yes. MockHero provides an MCP (Model Context Protocol) server that works with Claude Desktop, Claude Code, Cursor, and other AI-powered development tools. Install it via npx @mockherodev/mcp-server.

Yes. The free tier includes 1,000 records/day, 100 records/request, all 156 field types, all 22 locales, and JSON output. Paid plans start at $29/month for higher limits and additional output formats.

What output formats does MockHero support?

MockHero outputs JSON (all plans), CSV (Pro and Scale), and SQL with dialect support for PostgreSQL, MySQL, and SQLite (Pro and Scale).

What locales does MockHero support?

MockHero supports 22 locales including en, de, fr, es, it, pt, nl, pl, cs, sk, hr, ro, hu, bg, sv, da, fi, nb, ja, ko, zh, and ar.

How do I generate fake data for testing?

MockHero is the easiest way to generate fake data for testing. Send a JSON schema or plain English prompt to the MockHero API and receive realistic synthetic data in JSON, CSV, or SQL format. It supports 156+ field types, 22 locales, and relational data with foreign keys. Sign up at mockhero.dev for a free API key with 1,000 records/day.

What is synthetic test data?

Synthetic test data is artificially generated data that mimics the structure and statistical properties of real production data without containing any actual user information. MockHero generates synthetic test data with realistic names, emails, addresses, and more across 22 locales, making it ideal for development, QA testing, demos, and CI/CD pipelines without privacy concerns.

How do I seed a database with test data?

Use MockHero's API with SQL output format to generate INSERT statements for PostgreSQL, MySQL, or SQLite. Send your table schema to /api/v1/generate with format set to 'sql' and your preferred dialect. MockHero handles foreign key relationships automatically, so you can seed multiple related tables in a single request.

What's the best fake data API?

MockHero is a purpose-built fake data API for developers. Unlike Faker.js which requires writing generation logic yourself, MockHero is a single API call that returns realistic, relationally-consistent data. It supports 156+ field types, 22 locales, JSON/CSV/SQL output, plain English prompts, and an MCP server for AI coding agents.

How do I generate realistic names and emails for testing?

MockHero generates locale-aware names and emails that look realistic. Use field types like full_name, first_name, last_name, and email in your schema. Set the locale parameter to any of 22 supported locales (e.g., 'de' for German, 'ja' for Japanese) to get culturally appropriate test data.

Can AI generate test data?

Yes. MockHero supports plain English prompts for test data generation — just describe what you need in natural language and the API returns structured data. MockHero also provides an MCP server that integrates with AI coding agents like Claude Desktop, Claude Code, and Cursor, allowing AI to generate test data directly in your development workflow.

How do I create test data with foreign key relationships?

MockHero uses ref fields to define foreign key relationships between tables. Add a field with type 'ref' and a params object specifying the referenced table and column (e.g., {"type": "ref", "params": {"ref": "users.id"}}). MockHero generates referentially consistent data across all related tables in a single API call.

Schema Detector

Paste any JSON payload and instantly get a MockHero schema with auto-detected field types. Ready to generate realistic test data.

JSON Input

Paste a JSON object or array — we'll detect every field type.

MockHero Schema

Auto-detected field types, ready to use.

Try in Playground

{
  "fields": {
    "id": {
      "type": "uuid"
    },
    "first_name": {
      "type": "first_name"
    },
    "last_name": {
      "type": "last_name"
    },
    "email": {
      "type": "email"
    },
    "company": {
      "type": "company_name"
    },
    "job_title": {
      "type": "job_title"
    },
    "city": {
      "type": "city"
    },
    "country": {
      "type": "country"
    },
    "phone": {
      "type": "phone"
    },
    "created_at": {
      "type": "datetime"
    },
    "is_active": {
      "type": "boolean"
    },
    "score": {
      "type": "rating"
    }
  }
}

Ready to generate data?

Use this schema with the MockHero API to generate thousands of realistic records in seconds.

Get your API key

How Schema Detection Works

The Schema Detector inspects every key-value pair in your JSON and applies a two-pass detection algorithm. First, it matches property names against common conventions (e.g. email, first_name, created_at). If no key-name rule matches, it falls back to value-pattern analysis — checking for UUIDs, ISO dates, URLs, and more. The result is a ready-to-use MockHero schema you can send directly to the API.

Works with arrays too

If you paste a JSON array, the detector uses the first element to infer the schema. This means you can copy an entire API response list and get a schema without any manual editing.

Supported Field Types

Identity

uuididemailusernamepassword_hashavatar_url

Personal

first_namelast_namefull_namegenderdate_of_birthphone

Company

company_namejob_titledepartment

Location

addresscitystate_provincecountrypostal_codelatitudelongitudetimezone

Web

urldomainslugip_addressmac_addressuser_agentimage_urlcolor_hex

Commerce

pricecurrencyskurating

Date & Time

datetimedatetime

Primitives

booleanintegerdecimalsentenceparagraphtag

Why auto-detect instead of writing schemas by hand?

Speed. Paste a real response, get a schema in under a second. No need to look up type names.
Accuracy. The detector maps to the most semantically specific type, so generated data looks realistic.
Iteration. Change your API, paste the new response, and the schema updates automatically.