AgentPMT - The Agentic Economy

Synthetic Data Generator

Data
$0.10/requestMin Purchase: 20Min Purchase Price: $2

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

Parameters

(required properties listed first)

action
Type:string
Description:Use 'get_instructions' to retrieve documentation. Action to perform: generate
Default:generate
Allowed values:
get_instructionsgenerate
data_type
Type:string
Description:Type of synthetic data to generate. Options: 'person' (individual profiles with names, emails, addresses, demographics - for user registration, contact forms, CRM testing), 'company' (business profiles with industry, size, revenue, employees - for B2B systems, vendor management), 'family' (related family units with parents, children, shared addresses - for household data, family plans), 'technical' (IPs, UUIDs, URLs, domains, API keys - for network testing, API development), 'financial' (credit cards, bank accounts, transactions - for payment systems, banking apps), 'edge_cases' (unicode, special chars, injection patterns - for security testing, validation), 'ecommerce_dataset' (customers, products, orders, reviews - for e-commerce platforms), 'auth_system_dataset' (users, roles, permissions, sessions - for IAM/auth systems), 'crm_dataset' (companies, contacts, deals, pipeline - for CRM/sales platforms). Required for generate action.
Allowed values:
personcompanyfamilytechnicalfinancialedge_casesecommerce_datasetauth_system_datasetcrm_dataset
count
Type:integer
Description:Number of records to generate (1-1000). For simple types (person, company, family, technical, financial, edge_cases), this is the exact number of records. For datasets (ecommerce_dataset, auth_system_dataset, crm_dataset), this affects the size multiplier.
Default:1
Minimum:1
Maximum:1000
locale
Type:string
Description:Locale for region-specific data generation (names, addresses, phone formats). Supports: en_US (United States), en_GB (United Kingdom), de_DE (Germany), fr_FR (France), es_ES (Spain), it_IT (Italy), pt_BR (Brazil), nl_NL (Netherlands), pl_PL (Poland), ja_JP (Japan)
Default:en_US
Allowed values:
en_USen_GBde_DEfr_FRes_ESit_ITpt_BRnl_NLpl_PLja_JP
seed
Type:integer
Description:Random seed for reproducible results. Using the same seed with identical parameters generates the exact same data every time. Useful for: consistent test fixtures across environments, version-controlled test data, CI/CD pipelines, regression testing. Omit this field for random data each request.
include_details
Type:boolean
Description:Include extended details in generated data. For 'person': adds addresses, contact info, occupation, annual income. For 'company': adds employee lists, detailed financials. For 'family': adds relationship mappings between members. For datasets: includes all relationships (customer-order links, user-role assignments, etc). Set to false for minimal data.
Default:true
include_edge_cases
Type:boolean
Description:Mix in edge case data for robustness testing. Adds unicode characters (Chinese, Arabic, emojis), special characters, long strings, boundary values. Useful for testing: input validation, internationalization, XSS/injection protection, string length limits. Works with all data types.
size
Type:string
Description:Dataset size - ONLY applies to dataset types (ecommerce_dataset, auth_system_dataset, crm_dataset). 'small': ~100 total records (fast, for quick tests), 'medium': ~500 total records (balanced, standard testing), 'large': ~2000+ total records (comprehensive, load testing). Ignored for non-dataset types (person, company, etc).
Default:medium
Allowed values:
smallmediumlarge
options
Type:object
Description:Advanced type-specific options for customizing data generation. All nested options are optional. Available options by data_type: [person] age_range (array), [company] industry_filter (string), size_category (string), [family] family_size_range (array), [technical] data_types (array), [financial] currency (string), include_transactions (boolean), [edge_cases] severity_level (string), categories (array). See nested properties for details.
Default:[object Object]
Properties:
age_range
type:array
desc:[person only] Age range as [min_age, max_age]. Example: [25, 65] generates only people aged 25-65. Useful for: adult-only apps, senior care systems, age-restricted services. Array must have exactly 2 integers between 0-120.
items:integer
industry_filter
type:string
desc:[company only] Filter to specific industry. Generates only companies in this industry category. Options: 'Technology' (software, hardware, SaaS, AI/ML), 'Healthcare' (pharma, medical devices, biotech), 'Finance' (banking, insurance, fintech), 'Manufacturing' (automotive, aerospace, industrial), 'Retail' (e-commerce, fashion, grocery), 'Education' (K-12, higher education, EdTech).
enum:
TechnologyHealthcareFinanceManufacturingRetailEducation
size_category
type:string
desc:[company only] Company size category based on employee count. 'small': 1-50 employees (startups, SMBs), 'medium': 51-500 employees (mid-market), 'large': 501-5000 employees (large corporations), 'enterprise': 5000+ employees (Fortune 500).
enum:
smallmediumlargeenterprise
family_size_range
type:array
desc:[family only] Family size range as [min_members, max_members]. Example: [3, 5] generates families with 3-5 members. Includes parents and children. Default: [2, 6]. Array must have exactly 2 integers between 2-10.
items:integer
data_types
type:array
desc:[technical only] Specific technical data types to include. Options: 'ip' (IPv4 addresses), 'ipv6' (IPv6 addresses), 'mac' (MAC addresses), 'uuid' (UUID v1/v4), 'url' (full URLs), 'domain' (domain names), 'email' (email addresses), 'user_agent' (browser user agents), 'api_key' (API keys), 'token' (JWT tokens, session tokens). Omit to include common types (ip, uuid, url, email, user_agent).
items:string
currency
type:string
desc:[financial only] Currency code for financial data (ISO 4217 format). Affects number formatting and ranges. Examples: 'USD' (US Dollars), 'EUR' (Euros), 'GBP' (British Pounds), 'JPY' (Japanese Yen), 'CAD' (Canadian Dollars). Must be 3 uppercase letters.
default:USD
include_transactions
type:boolean
desc:[financial only] Include detailed transaction history. When true, each financial record includes 5-50 transactions with dates, amounts, merchants, categories. Increases data size significantly but provides realistic spending patterns. Useful for: transaction analysis, fraud detection testing, financial reporting.
default:false
severity_level
type:string
desc:[edge_cases only] Edge case severity level. 'low': mild edge cases (basic unicode, short long strings), 'medium': moderate edge cases (balanced testing), 'high': extreme edge cases (maximum boundary testing, all edge case types, largest data). Use 'high' for comprehensive security/validation testing.
enum:
lowmediumhigh
default:medium
categories
type:array
desc:[edge_cases only] Specific edge case categories to generate. Options: 'unicode' (Chinese, Arabic, Cyrillic, emojis), 'length' (empty strings, single chars, very long strings), 'null' (null values, empty objects/arrays), 'boundary' (max/min integers, float precision issues), 'malformed' (invalid emails, URLs, dates), 'injection' (SQL injection patterns, XSS attempts - safe for testing), 'special_chars' (all special characters), 'numeric' (division by zero, Infinity, NaN). Omit to include all categories.
items:string

API Example

curl -X POST "https://api.agentpmt.com/products/purchase" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your-api-key-here" \
  -H "X-Budget-Key: your-budget-key-here" \
  -d '{
    "product_id": "69488f73b54506f955d789e8",
    "parameters": {
      "action": "generate",
      "count": 1,
      "locale": "en_US",
      "include_details": true,
      "size": "medium",
      "options": "[object Object]"
    }
  }'

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About The Developer

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Apoth3osis

★13 stars

Joined Agent Payment: August 14, 2025

We build tools that enable AI agents to excel in the mathematical realm.

Our small team develops experimental and unique solutions in the AI arena, with a strong focus on modular computing for agentic applications and custom model deployment. We have handled projects for a variety of applications across many sectors, from algorithmic trading and financial analysis, to molecular simulations and predictions, to habitat and biodiversity monitoring and wildlife conservation.