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.

How can AI help generate test data?

MockHero supports plain English prompts for test data generation and provides an MCP server for AI coding agents like Claude Desktop, Claude Code, and Cursor. Set up MockHero's MCP server in Windsurf IDE for seamless test data generation. Let Windsurf's Cascade agent create realistic, relational data while you code.

What is MCP for test data generation?

MCP (Model Context Protocol) lets AI coding agents call MockHero directly. Install the MockHero MCP server via npx @mockherodev/mcp-server and your AI assistant can generate realistic test data with 156+ field types, relational integrity, and multiple output formats.

Can I describe my test data in plain English?

Yes. MockHero accepts plain English prompts alongside JSON schemas. Describe the data you need in natural language, and MockHero returns structured, realistic test data. This works both through the API and through AI coding agents via the MCP server.

Generate Test Data in Windsurf with MockHero MCP

The Problem

Windsurf's Cascade agent excels at understanding your entire codebase and making multi-file edits. But when it generates test data, the results are often shallow. You get a few hardcoded records that are enough to make the code compile but not enough to test real-world scenarios like pagination, search filtering, or edge cases in business logic.

Building a proper seed script manually defeats the purpose of using an AI-powered editor. You want the agent to handle the boring parts, including generating realistic data that actually exercises your application.

The Solution: MockHero MCP in Windsurf

Windsurf supports MCP tool servers, allowing Cascade to call external services. By adding MockHero as an MCP server, Cascade can generate realistic, relationally-consistent test data directly within your workflow. Describe your data needs in the chat, and Cascade does the rest.

Quick Setup

Add MockHero to your Windsurf MCP configuration:

{
  "mcpServers": {
    "mockhero": {
      "command": "npx",
      "args": ["-y", "@mockhero/mcp-server"],
      "env": {
        "MOCKHERO_API_KEY": "mh_your_api_key"
      }
    }
  }
}

Save the configuration and restart Windsurf. Cascade will detect the MockHero tool automatically.

Step-by-Step Guide

1. Get your MockHero API key

2. Configure MCP in Windsurf

Open Windsurf's MCP settings and add the MockHero server configuration shown above. The server runs via npx so there are no global installations needed.

3. Restart and verify

Restart Windsurf and open the Cascade panel. You should see MockHero listed as an available tool. If it does not appear, check that your API key is set correctly.

4. Ask Cascade to generate data

Use natural language in the Cascade chat:

"I need a seed script for my SaaS app. Generate 10 organizations,
50 users across those orgs, and 200 projects with realistic names
and descriptions. Use my Supabase database."

Cascade will call MockHero, receive the data, and write a seed script tailored to your project's database setup.

5. Execute and test

Cascade can run the seed script from Windsurf's terminal. Open your database GUI to verify the data and its relationships.

Why MockHero MCP in Windsurf vs Alternatives

Cascade-native — works within Windsurf's agentic workflow. No context switching to external tools.
Codebase-aware — Cascade understands your schema files and can generate MockHero requests that match your actual tables.
Production-realistic — 156+ field types produce data that looks like a real production database, not placeholder text.
Multi-table — generate complex schemas with multiple related tables in a single request.

Get Started

Give Windsurf's Cascade the ability to generate real test data. Sign up free at mockhero.dev, add the MCP config, and let your AI agent handle test data while you focus on building features.