Synthetic Data Generator by Apoth3osis

Name: Synthetic Data Generator
Brand: Apoth3osis
SKU: 69488f73b54506f955d789e8
Price: 0.10 USD
Availability: InStock

Synthetic Data Generator

Data

Available ActionsEach successful request consumes credits as outlined below.

generate^10cr

Details

The Synthetic Data Generator creates production-quality fake data for development, testing, and demonstrations. Data Types The tool generates nine data types. Person Profiles include demographics, contact info, and addresses. Company Profiles contain industry classification, revenue, employee counts, and org structures. Family Units provide related household members with shared addresses and relationship mappings. Technical Data covers IPs, UUIDs, MAC addresses, URLs, domains, API keys, and system information. Financial Data generates fake credit cards, bank accounts, transactions, and investment portfolios. Edge Cases produce boundary testing data with unicode, special characters, injection patterns, and null values. E-commerce Datasets create complete online store ecosystems with customers, products, orders, and reviews. Auth System Datasets provide full IAM data with users, roles, permissions, sessions, and audit logs. CRM Datasets generate sales pipeline data with companies, contacts, leads, opportunities, and deals. Locale Support Data can be generated in 10 locales: en_US, en_GB, de_DE, fr_FR, es_ES, it_IT, pt_BR, nl_NL, pl_PL, and ja_JP. Complexity Control Simple mode requires only data type and count. Detailed mode enables extended fields and relationships. Dataset sizes range from small (100 records) to medium (500) to large (2000+). Advanced options provide granular control over age ranges, industries, family sizes, currencies, and more. Testing Features Edge case testing includes unicode, boundary values, special characters, and injection patterns at low, medium, or high severity levels. The generator maintains realistic relationships such as parent-child, customer-order, and user-role mappings. Security testing patterns include SQL injection, XSS attempts, and malformed data. Scale Simple types support 1–1000 records per request. Datasets generate hundreds to thousands of related records with preserved relationships and data integrity across entities.

Use Cases

Generate realistic customer data, usage patterns, and transaction history, edge cases to validate UI robustness, large datasets for stress testing, valid-format credit card numbers

Dynamic MCP Setup

Connect once through AgentPMT Dynamic MCP, then use approved tools from the same agent connection.

30 Second Setup

STDIO connector for Claude Code, Codex, Cursor, Zed, and other LLMs that require STDIO or custom connections.

npm install -g @agentpmt/mcp-routeragentpmt-setup

Hosted Streamable HTTPS

MCP endpoint for browser-based apps like ChatGPT, Claude, Grok, or any time you want a streamable connection with no local install.

https://api.agentpmt.com/mcp

Config Example

Use the hosted endpoint directly in clients that support remote MCP. Store your Bearer token in the client config or secret field.

Full connection guide

{
  "mcpServers": {
    "agentpmt": {
      "type": "streamable-http",
      "url": "https://api.agentpmt.com/mcp",
      "headers": {
        "Authorization": "Bearer <AGENTPMT_BEARER_TOKEN>",
        "x-instance-metadata": "{\"client\":\"generic-mcp\",\"platform\":\"remote\"}"
      }
    }
  }
}

Need client videos, organization controls, audit details, and the full feature overview?

More About Dynamic MCP

Actions(1)

generate^10cr8 params(1 required)

Generate synthetic data of a specified type. Supports person profiles, company profiles, family units, technical data, financial data, edge cases, and complete relational datasets (e-commerce, auth system, CRM).

data_typerequiredstring

Type of synthetic data to generate. Options: 'person' (individual profiles with names, emails, addresses, demographics), 'company' (business profiles with industry, size, revenue, employees), 'family' (related family units with parents, children, shared addresses), 'technical' (IPs, UUIDs, URLs, domains, API keys), 'financial' (credit cards, bank accounts, transactions), 'edge_cases' (unicode, special chars, injection patterns for security testing), 'ecommerce_dataset' (customers, products, orders, reviews), 'auth_system_dataset' (users, roles, permissions, sessions), 'crm_dataset' (companies, contacts, deals, pipeline).

Values:

personcompanyfamilytechnicalfinancialedge_casesecommerce_datasetauth_system_datasetcrm_dataset

countinteger

Number of records to generate (1-1000). For simple types this is exact record count. For dataset types, this affects the size multiplier.

Default: 1

Range: 1 - 1000

localestring

Locale for region-specific data generation (names, addresses, phone formats). Supports: en_US, en_GB, de_DE, fr_FR, es_ES, it_IT, pt_BR, nl_NL, pl_PL, ja_JP.

Values:

en_USen_GBde_DEfr_FRes_ESit_ITpt_BRnl_NLpl_PLja_JP

Default: en_US

seedinteger

Random seed for reproducible results. Same seed + same parameters = same data every time. Omit for random data each request.

include_detailsboolean

Include extended details in generated data. For 'person': adds addresses, contact info, occupation. For 'company': adds employee lists. For 'family': adds relationship mappings. For datasets: includes all relationships. Set to false for minimal data.

Default: true

include_edge_casesboolean

Mix in edge case data for robustness testing. Adds unicode characters, special characters, long strings, boundary values. Works with all data types.

Default: false

sizestring

Dataset size - ONLY for dataset types (ecommerce_dataset, auth_system_dataset, crm_dataset). 'small': ~100 records, 'medium': ~500 records, 'large': ~2000+ records. Ignored for non-dataset types.

Values:

smallmediumlarge

Default: medium

optionsobject

Advanced type-specific options. Available by data_type: [person] age_range, [company] industry_filter/size_category, [family] family_size_range, [technical] data_types, [financial] currency/include_transactions, [edge_cases] severity_level/categories.

Properties:

age_range(array)- [person only] Age range as [min_age, max_age]. Example: [25, 65].

industry_filter(string)- [company only] Filter to specific industry.

size_category(string)- [company only] Company size: small (1-50), medium (51-500), large (501-5000), enterprise (5000+).

family_size_range(array)- [family only] Family size range as [min, max]. Default: [2, 6].

data_types(array)- [technical only] Technical data types to include.

currency(string)- [financial only] ISO 4217 currency code. Default: USD.

include_transactions(boolean)- [financial only] Include transaction history. Default: false.

severity_level(string)- [edge_cases only] Severity: low, medium, high.

categories(array)- [edge_cases only] Categories to generate.

curl -X POST "https://api.agentpmt.com/products/purchase" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ********" \
  -d '{
    "product_id": "69488f73b54506f955d789e8",
    "parameters": {
      "action": "generate",
      "data_type": "person",
      "count": 1,
      "locale": "en_US",
      "include_details": true,
      "include_edge_cases": false,
      "size": "medium"
    }
  }'

import requests
import json

url = "https://api.agentpmt.com/products/purchase"

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer ********"
}

data = {
    "product_id": "69488f73b54506f955d789e8",
    "parameters": {
        "action": "generate",
        "data_type": "person",
        "count": 1,
        "locale": "en_US",
        "include_details": true,
        "include_edge_cases": false,
        "size": "medium"
    }
}

response = requests.post(url, headers=headers, json=data)
print(response.status_code)
print(response.json())

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "69488f73b54506f955d789e8",
  parameters: {
    "action": "generate",
    "data_type": "person",
    "count": 1,
    "locale": "en_US",
    "include_details": true,
    "include_edge_cases": false,
    "size": "medium"
  }
};

fetch(url, {
  method: "POST",
  headers,
  body: JSON.stringify(data)
})
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error("Error:", error));

const axios = require('axios');

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "69488f73b54506f955d789e8",
  parameters: {
    "action": "generate",
    "data_type": "person",
    "count": 1,
    "locale": "en_US",
    "include_details": true,
    "include_edge_cases": false,
    "size": "medium"
  }
};

axios.post(url, data, { headers })
  .then(response => {
    console.log(response.status);
    console.log(response.data);
  })
  .catch(error => {
    console.error("Error:", error.message);
  });

Login to view your API and budget keys. The example above uses placeholder values. Sign in to see personalized code with your bearer token.

Autonomous agents can access this tool through AgentAddress credit balances or direct x402 payments. Use the Autonomous Agent API reference for endpoint shapes after choosing the access pattern below.

Recommended

Credit-Based Access Using AgentAddress

AgentAddress is preferred when an autonomous agent needs persistent file access, stored platform state, or maximum tool use ability across repeated calls.

Open Credit-Based Access Using AgentAddress

Direct x402 Payment

Use direct x402 for independent one-off tool calls that do not require shared files or stored platform state.

Accepted public payments

Stablecoin: USDC
Chains: Base, Arbitrum, Optimism, Polygon, and Avalanche

Direct x402 payments are not enabled for this product; use AgentAddress credit access instead.

Product Skill Package

This product has a published Agent Skill package. Install it when an autonomous agent needs product-specific operating instructions in its local skill registry.

Download SKILL.md View package source OpenClaw listing

OpenClaw install

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skills.sh install

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Usage Instructions

Usage guidance provided directly by the developer for this product.

Synthetic Data Generator - Instructions

Overview

Generate realistic synthetic data for testing, development, and prototyping. Supports individual records (person, company, family, technical, financial, edge cases) and complete relational datasets (e-commerce, auth system, CRM). Data is locale-aware with support for 10 regions. Use a seed for reproducible results across runs.

Actions

generate

Generate synthetic data of a specified type.

Required Fields:

action (string): "generate"
data_type (string): Type of data to generate. One of: person, company, family, technical, financial, edge_cases, ecommerce_dataset, auth_system_dataset, crm_dataset

Optional Fields:

count (integer, default: 1): Number of records to generate (1-1000). For dataset types, this is ignored in favor of the size parameter.
locale (string, default: "en_US"): Region for names, addresses, phone formats. Options: en_US, en_GB, de_DE, fr_FR, es_ES, it_IT, pt_BR, nl_NL, pl_PL, ja_JP
seed (integer, default: null): Random seed for reproducible output. Same seed + same parameters = same data every time. Omit for random data.
include_details (boolean, default: true): Include extended fields (addresses, financials, relationships). Set false for minimal records.
include_edge_cases (boolean, default: false): Mix in unicode, special characters, and boundary values for robustness testing.
size (string, default: "medium"): Only for dataset types (ecommerce_dataset, auth_system_dataset, crm_dataset). Options: small (~100 records), medium (~500 records), large (~2000+ records). Ignored for non-dataset types.
options (object): Type-specific advanced options (see below).

Data Types

person

Individual profiles with names, emails, addresses, demographics.

Options:

age_range (array of 2 integers, 0-120): Filter by age range, e.g. [25, 65]

Example:

{
  "action": "generate",
  "data_type": "person",
  "count": 5,
  "locale": "fr_FR",
  "options": { "age_range": [25, 45] }
}

company

Business profiles with industry, size, revenue, and sample employees.

Options:

industry_filter (string): Filter to a specific industry. Options: Technology, Healthcare, Finance, Manufacturing, Retail, Education
size_category (string): Company size. Options: small (1-50 employees), medium (51-500), large (501-5000), enterprise (5000+)

Example:

{
  "action": "generate",
  "data_type": "company",
  "count": 3,
  "options": { "industry_filter": "Technology", "size_category": "medium" }
}

family

Family units with parents, children, shared addresses, and relationship mappings.

Options:

family_size_range (array of 2 integers, 2-10): Min and max family members, e.g. [3, 5]

Example:

{
  "action": "generate",
  "data_type": "family",
  "count": 2,
  "include_details": true,
  "options": { "family_size_range": [3, 5] }
}

technical

IPs, UUIDs, URLs, domains, API keys, tokens, and system info.

Options:

data_types (array of strings): Which technical types to include. Options: ip, ipv6, mac, uuid, url, domain, email, user_agent, api_key, token. Default: ["ip", "uuid", "url", "email", "user_agent"]

Example:

{
  "action": "generate",
  "data_type": "technical",
  "count": 10,
  "options": { "data_types": ["ip", "ipv6", "mac", "uuid", "api_key"] }
}

financial

Credit cards, bank accounts, balances, and transaction history.

Options:

currency (string, default: "USD"): ISO 4217 currency code (e.g. USD, EUR, GBP, JPY)
include_transactions (boolean, default: false): Include 5-50 detailed transactions per record

Example:

{
  "action": "generate",
  "data_type": "financial",
  "count": 3,
  "options": { "currency": "EUR", "include_transactions": true }
}

edge_cases

Unicode, special characters, boundary values, injection patterns, and malformed data for robustness testing.

Options:

severity_level (string, default: "medium"): Options: low, medium, high. Higher severity produces more extreme test values.
categories (array of strings): Which edge case types to include. Options: unicode, length, null, boundary, malformed, injection, special_chars, numeric. Default: ["unicode", "length", "null", "boundary"]

Example:

{
  "action": "generate",
  "data_type": "edge_cases",
  "count": 5,
  "options": { "severity_level": "high", "categories": ["unicode", "injection", "boundary", "malformed"] }
}

ecommerce_dataset

Complete e-commerce dataset with interlinked customers, products, and orders.

Example:

{
  "action": "generate",
  "data_type": "ecommerce_dataset",
  "size": "small",
  "seed": 42
}

auth_system_dataset

Authentication/authorization dataset with users, roles, permissions, and sessions.

Example:

{
  "action": "generate",
  "data_type": "auth_system_dataset",
  "size": "medium",
  "locale": "en_GB"
}

crm_dataset

CRM dataset with companies, contacts, and deals/opportunities with pipeline stages.

Example:

{
  "action": "generate",
  "data_type": "crm_dataset",
  "size": "large",
  "seed": 123
}

Common Workflows

Reproducible Test Fixtures

Use seed to generate identical data across environments:

{
  "action": "generate",
  "data_type": "person",
  "count": 100,
  "seed": 12345,
  "locale": "en_US"
}

Security and Validation Testing

Combine edge cases with regular data:

{
  "action": "generate",
  "data_type": "person",
  "count": 50,
  "include_edge_cases": true
}

Minimal Data for Quick Tests

Disable extended details for lightweight records:

{
  "action": "generate",
  "data_type": "company",
  "count": 20,
  "include_details": false
}

Important Notes

count ranges from 1 to 1000. For dataset types, use size instead to control volume.
Dataset types (ecommerce_dataset, auth_system_dataset, crm_dataset) return multiple interlinked collections with a record_counts summary.
Single-record requests (count=1) return the object directly; multiple records return a list wrapped in a named key.
All generated data is fake and safe for testing. Financial data (credit cards, accounts) is not real.
Edge case injection patterns are safe strings for testing input validation -- they do not perform actual attacks.
The locale parameter affects names, addresses, and phone number formats but not all fields (e.g., currency must be set separately via options).

Frequently Asked Questions

How do I connect this tool to an external agent?

You can install the local MCP server by opening a terminal and running:

Install commands

npm install -g @agentpmt/mcp-router
agentpmt-setup

This will connect you to local agents like Claude Code, Windsurf, Grok Build, Cursor, etc.

Alternatively you can connect to the hosted version with this config block, no installation required:

Hosted MCP config

{
  "mcpServers": {
    "agentpmt": {
      "type": "streamable-http",
      "url": "https://api.agentpmt.com/mcp",
      "headers": {
        "Authorization": "Bearer <AGENTPMT_BEARER_TOKEN>",
        "x-instance-metadata": "{\"client\":\"generic-mcp\",\"platform\":\"remote\"}"
      }
    }
  }
}

View MCP Connection Instructions for more details.

How does an external agent use this tool?

After the external agent is connected to an Agent Group that can use this tool, paste this prompt into the agent:

Agent prompt

Use the AgentPMT-Tool-Search-and-Execution tool. First call action 'get_instructions' so you know how to use the tool search interface. Then call action 'get_schema' with tool_id 69488f73b54506f955d789e8 ("Synthetic Data Generator"). After reading the schema and any returned instructions, tell me what this tool can do, we are going to be using it

The agent should fetch the tool schema first, collect the required parameters for your request, and then call the tool through AgentPMT.

Synthetic Data Generator

Available ActionsEach successful request consumes credits as outlined below.

Details

Use Cases

Dynamic MCP Setup

30 Second Setup

Hosted Streamable HTTPS

Config Example

Frequently Asked Questions

How do I connect this tool to an external agent?

How does an external agent use this tool?

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