# Mockaroo MCP

> Mockaroo gives your AI agent professional-grade data synthesis capabilities. It generates thousands of rows of realistic, diverse dummy records instantly, allowing you to audit schemas and discover field types right from conversation. Stop building test environments with static spreadsheets; generate high-fidelity mock data tailored exactly to your needs.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Tags:** test-data, data-synthesis, csv-export, json-generation, schema-design, dummy-data

## Description

Mockaroo lets your AI client handle the entire process of creating complex datasets. Instead of manually writing out fields or importing messy CSVs, you talk to your agent about what kind of data you need—say, 50 user profiles with unique names and valid addresses. The agent then uses this MCP to instantly synthesize those records in JSON format. It’s like having a real-time data architect available only through natural conversation. You can also browse saved schemas or check the catalog for field types without ever opening a technical configuration page. This capability makes it easy to build robust prototypes or test application performance using high-quality, diverse data. All of this is managed and accessed through Vinkius, making Mockaroo one part of many powerful tools available to your agent.

## Tools

### generate_mock_data
Generate dummy data based on a list of fields

### generate_from_schema
Generate data using a saved schema name

### list_datasets
List uploaded datasets in Mockaroo

### list_schemas
List saved schemas in your Mockaroo account

### list_field_types
List all available field types for generation

## Prompt Examples

**Prompt:** 
```
Generate 10 rows of mock data with 'id' (Row Number) and 'name' (Full Name) using Mockaroo.
```

**Response:** 
```
I've generated 10 records for you! The data includes unique IDs and realistic names like 'John Doe' and 'Jane Smith'. Would you like the JSON output or more rows?
```

**Prompt:** 
```
List all my saved schemas in Mockaroo.
```

**Response:** 
```
I've retrieved your schemas. You have 5 saved structures, including 'User Profile' and 'Sales Report'. Which one would you like to use for data generation?
```

**Prompt:** 
```
Generate 50 rows using my schema named 'TestUsers'.
```

**Response:** 
```
Data generation complete! I've retrieved 50 rows following the 'TestUsers' structure. I can provide a summary of the generated records if you'd like.
```

## Capabilities

### Generate records from fields
Create a specific number of fake data rows using a defined list of field types.

### Generate records from saved structures
Use a previously configured schema name to generate consistent, structured test data batches.

### List available schemas
See all the mockaroo blueprints you've saved for future data generation.

### Check uploaded datasets
View and manage any reference or dataset files you’ve already loaded into your account.

### List field types
Get a full list of all possible data markers (like 'email' or 'date') available for building records.

## Use Cases

### Testing user signup flows
A QA engineer needs to verify that the signup endpoint handles different regions. They ask their agent to run `generate_mock_data` with fields like 'country code', 'full name', and 'email' to ensure global validation works correctly.

### Building a prototype user dashboard
A Product Manager needs sample data for a pitch. Instead of using fake placeholders, they prompt the agent to `generate_from_schema` based on their 'User Profile' template, getting instantly structured JSON ready for presentation.

### Verifying API schema changes
A Backend Developer updates an endpoint and needs to confirm field types. They prompt the agent to use `list_field_types` first, then generate records using a custom schema, verifying that data types match expectations.

### Auditing legacy reference data
An Operations Lead wants to know what historical datasets are available for migration testing. They use the `list_datasets` tool to get an inventory of all uploaded reference files before starting work.

## Benefits

- Stop guessing what data looks like. You can use the `list_field_types` tool to see every available marker, ensuring your test coverage is comprehensive.
- Consistency matters for testing. Use `generate_from_schema` to pull records that always adhere to a specific, reliable structure you've pre-defined.
- Need data fast? Generating hundreds of realistic rows via the `generate_mock_data` tool lets your agent build test cases without manual effort.
- Maintain control over your inputs. The MCP allows you to use `list_datasets` to track and reference specific files for cross-functional testing.
- Your AI client acts like a data architect, handling schema audits and field type discovery through simple conversation rather than complex UI clicks.

## How It Works

The bottom line is you get clean, high-volume test data without writing a single query or touching a setup screen.

1. Subscribe to this MCP and provide your Mockaroo API key within your agent’s settings.
2. Instruct your AI client to perform a data task, like 'Generate 10 records for user testing using my saved schema.'
3. The MCP executes the request, returning structured mock data (JSON) directly to your conversational thread.

## Frequently Asked Questions

**How do I generate mock data with Mockaroo using my AI client?**
You simply instruct your agent to create records, specifying the number of rows and which fields you want. The agent handles calling `generate_mock_data` and gives you JSON right away.

**What is the difference between generating data with Mockaroo's schemas vs. specific fields?**
Using a saved schema (`generate_from_schema`) guarantees consistency because it follows a defined blueprint. Using specific fields lets you customize and build records ad-hoc for one-off tests.

**Does Mockaroo help me find what kinds of data I can use?**
Yes, the `list_field_types` tool pulls a comprehensive catalog of every available marker—everything from 'zip code' to 'full name'—so you know exactly what your test suite can handle.

**Can I list my saved mockaroo schemas in Mockaroo?**
Absolutely. Use the `list_schemas` tool. This lets you audit all the data structures you've built and keeps them organized for later use when generating new test batches.

**Is Mockaroo suitable for large-scale testing?**
Yes, it excels at scale. You can generate thousands of records instantly using `generate_mock_data` without having to manually build or copy the data sets.

**How do I find my Mockaroo API Key?**
Log in to your [**Mockaroo account**](https://mockaroo.com/api_keys), and you will find your API Key on the API Keys page. Copy and paste it below.

**Can the agent use my saved schemas?**
Yes. Use the `generate_from_schema` tool providing the name of your saved schema. Your agent will generate data following that specific structure instantly.

**Is it possible to list all available field types?**
Yes. The `list_field_types` tool returns the full catalog of Mockaroo field types, allowing you to audit available markers for your data generation.