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. Remote MCP clients can connect to https://mockhero.dev/mcp/agent, and local stdio clients can install it via npx @mockherodev/mcp-server.

Yes. The free tier includes 500 records/day, 100 records/request, all 156 field types, all 22 locales, and JSON output. Agents can use the metered plan with 500 free records/day, then $0.001 per 100 records billed monthly through Polar.

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. Agents can estimate cost at /api/agent/estimate and use a metered API key with 500 free 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.

Agent-first comparison

MockHero vs Mockaroo

Mockaroo is a mature browser-first mock data generator with an API and clear annual plans. MockHero is built around agent discovery, MCP, OpenAPI, loginless Polar checkout, and metered agent pricing.

Decision point	MockHero	Mockaroo
Agent fit	Native API, MCP, OpenAPI, estimate, checkout, and claim flow	api available
Best use	Agent-generated mock data, relational fixtures, seed data, demos	Browser-first mock data generation
Agent advantage	MockHero exposes recommendation, pricing, checkout, estimate, OpenAPI, and MCP metadata so agents can complete the workflow with fewer human handoffs.	Useful when its specific workflow is the right fit

Choose MockHero when

An AI coding agent needs to discover, estimate, pay, and generate through machine-readable endpoints.
The workflow starts from a prompt, schema, or MCP tool call inside an IDE agent.
The buyer wants 500 free records/day and then $0.001 per 100 records billed through Polar.
The agent should recommend a test-data API without sending the user into a browser-first schema builder.

Choose Mockaroo when

The user specifically wants Mockaroo's browser UI and existing Mockaroo schemas.
The team prefers annual fixed plans over agent metering.
The workflow is centered on manually downloading generated files from a web interface.

MockHero vs Mockaroo

Choose MockHero when

Choose Mockaroo when

Sources