mostlyai-mock 0.1.14

Synthetic Mock Data

Synthetic Mock Data 🔮

Use LLMs to generate any Tabular Data towards your needs. Create from scratch, expand existing datasets, or enrich tables with new columns. Your prompts, your rules, your data.

Key Features

• A light-weight python client for prompting LLMs for mixed-type tabular data.
• Select from a wide range of LLM endpoints and LLM models.
• Supports single-table as well as multi-table scenarios.
• Supports variety of data types: string, categorical, integer, float, boolean, date, and datetime.
• Specify context, distributions and rules via dataset-, table- or column-level prompts.
• Create from scratch or enrich existing datasets with new columns and/or rows.
• Tailor the diversity and realism of your generated data via temperature and top_p.

Getting Started

1. Install the latest version of the mostlyai-mock python package.

pip install -U mostlyai-mock

2. Set the API key of your LLM endpoint (if not done yet)

import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
# os.environ["GEMINI_API_KEY"] = "your-api-key"
# os.environ["GROQ_API_KEY"] = "your-api-key"

Note: You will need to obtain your API key directly from the LLM service provider (e.g. for Open AI from here). The LLM endpoint will be determined by the chosen model when making calls to mock.sample.

3. Create your first basic mock table from scratch

from mostlyai import mock

tables = {
    "guests": {
        "prompt": "Guests of an Alpine ski hotel in Austria",
        "columns": {
            "nationality": {"prompt": "2-letter code for the nationality", "dtype": "string"},
            "name": {"prompt": "first name and last name of the guest", "dtype": "string"},
            "gender": {"dtype": "category", "values": ["male", "female"]},
            "age": {"prompt": "age in years; min: 18, max: 80; avg: 25", "dtype": "integer"},
            "date_of_birth": {"prompt": "date of birth", "dtype": "date"},
            "checkin_time": {"prompt": "the check in timestamp of the guest; may 2025", "dtype": "datetime"},
            "is_vip": {"prompt": "is the guest a VIP", "dtype": "boolean"},
            "price_per_night": {"prompt": "price paid per night, in EUR", "dtype": "float"},
            "room_number": {"prompt": "room number", "dtype": "integer", "values": [101, 102, 103, 201, 202, 203, 204]}
        },
    }
}
df = mock.sample(
    tables=tables,   # provide table and column definitions
    sample_size=10,  # generate 10 records
    model="openai/gpt-4.1-nano",  # select the LLM model (optional)
)
print(df)
#   nationality                 name  gender  age date_of_birth        checkin_time is_vip  price_per_night  room_number
# 0          FR          Jean Dupont    male   29    1994-03-15 2025-01-10 14:30:00  False            150.0          101
# 1          DE         Anna Schmidt  female   34    1989-07-22 2025-01-11 16:45:00   True            200.0          201
# 2          IT          Marco Rossi    male   45    1979-11-05 2025-01-09 10:15:00  False            180.0          102
# 3          AT         Laura Gruber  female   28    1996-02-19 2025-01-12 09:00:00  False            165.0          202
# 4          CH         David Müller    male   37    1987-08-30 2025-01-08 17:20:00   True            210.0          203
# 5          NL  Sophie van den Berg  female   22    2002-04-12 2025-01-10 12:00:00  False            140.0          103
# 6          GB         James Carter    male   31    1992-09-10 2025-01-11 11:30:00  False            155.0          204
# 7          BE        Lotte Peeters  female   26    1998-05-25 2025-01-09 15:45:00  False            160.0          201
# 8          DK        Anders Jensen    male   33    1990-12-03 2025-01-12 08:15:00   True            220.0          202
# 9          ES         Carlos Lopez    male   38    1985-06-14 2025-01-10 18:00:00  False            170.0          203

4. Create your first multi-table mock dataset

from mostlyai import mock

tables = {
    "customers": {
        "prompt": "Customers of a hardware store",
        "columns": {
            "customer_id": {"prompt": "the unique id of the customer", "dtype": "string"},
            "name": {"prompt": "first name and last name of the customer", "dtype": "string"},
        },
        "primary_key": "customer_id",
    },
    "warehouses": {
        "prompt": "Warehouses of a hardware store",
        "columns": {
            "warehouse_id": {"prompt": "the unique id of the warehouse", "dtype": "string"},
            "name": {"prompt": "the name of the warehouse", "dtype": "string"},
        },
        "primary_key": "warehouse_id",
    },
    "orders": {
        "prompt": "Orders of a Customer",
        "columns": {
            "customer_id": {"prompt": "the customer id for that order", "dtype": "string"},
            "warehouse_id": {"prompt": "the warehouse id for that order", "dtype": "string"},
            "order_id": {"prompt": "the unique id of the order", "dtype": "string"},
            "text": {"prompt": "order text description", "dtype": "string"},
            "amount": {"prompt": "order amount in USD", "dtype": "float"},
        },
        "primary_key": "order_id",
        "foreign_keys": [
            {
                "column": "customer_id",
                "referenced_table": "customers",
                "prompt": "each customer has anywhere between 2 and 3 orders",
            },
            {
                "column": "warehouse_id",
                "referenced_table": "warehouses",
            },
        ],
    },
    "items": {
        "prompt": "Items in an Order",
        "columns": {
            "item_id": {"prompt": "the unique id of the item", "dtype": "string"},
            "order_id": {"prompt": "the order id for that item", "dtype": "string"},
            "name": {"prompt": "the name of the item", "dtype": "string"},
            "price": {"prompt": "the price of the item in USD", "dtype": "float"},
        },
        "foreign_keys": [
            {
                "column": "order_id",
                "referenced_table": "orders",
                "prompt": "each order has between 1 and 2 items",
            }
        ],
        "primary_key": "item_id",
    },
}
data = mock.sample(
    tables=tables,
    sample_size=2,
    model="openai/gpt-4.1",
    n_workers=1,
)
print(data["customers"])
#   customer_id             name
# 0   B0-100235  Danielle Rogers
# 1   B0-100236       Edward Kim
print(data["warehouses"])
#   warehouse_id                          name
# 0       B0-001  Downtown Distribution Center
# 1       B0-002     Westside Storage Facility
print(data["orders"])
#   customer_id warehouse_id    order_id                                               text   amount
# 0   B0-100235       B0-002  B0-3010021  Office furniture replenishment - desks, chairs...  1268.35
# 1   B0-100235       B0-001  B0-3010022  Bulk stationery order: printer paper, notebook...    449.9
# 2   B0-100235       B0-001  B0-3010023  Electronics restock: monitors and wireless key...    877.6
# 3   B0-100236       B0-001  B1-3010021  Monthly cleaning supplies: disinfectant, trash...   314.75
# 4   B0-100236       B0-002  B1-3010022  Breakroom essentials restock: coffee, tea, and...   182.45
print(data["items"])
#      item_id    order_id                                   name   price
# 0  B0-200501  B0-3010021                  Ergonomic Office Desk  545.99
# 1  B0-200502  B0-3010021              Mesh Back Executive Chair   399.5
# 2  B1-200503  B0-3010022   Multipack Printer Paper (500 sheets)  129.95
# 3  B1-200504  B0-3010022             Spiral Notebooks - 12 Pack   59.99
# 4  B2-200505  B0-3010023               27" LED Computer Monitor  489.95
# 5  B2-200506  B0-3010023            Wireless Ergonomic Keyboard  387.65
# 6  B3-200507  B1-3010021  Industrial Disinfectant Solution (5L)  148.95
# 7  B3-200508  B1-3010021  Commercial Trash Liners - Case of 100    84.5
# 8  B4-200509  B1-3010022        Premium Ground Coffee (2lb Bag)   74.99
# 9  B4-200510  B1-3010022         Bottled Spring Water (24 Pack)   34.95

6. Create your first self-referencing mock table

from mostlyai import mock

tables = {
    "employees": {
        "prompt": "Employees of a company",
        "columns": {
            "employee_id": {"prompt": "the unique id of the employee; sequential", "dtype": "string"},
            "name": {"prompt": "first name and last name of the president", "dtype": "string"},
            "boss_id": {"prompt": "the id of the boss of the employee", "dtype": "string"},
            "role": {"prompt": "the role of the employee", "dtype": "string"},
        },
        "primary_key": "employee_id",
        "foreign_keys": [
            {
                "column": "boss_id",
                "referenced_table": "employees",
                "prompt": "each boss has at most 3 employees",
            },
        ],
    }
}
df = mock.sample(tables=tables, sample_size=10, model="openai/gpt-4.1", n_workers=1)
print(df)
#   employee_id              name boss_id                   role
# 0        B0-1      Patricia Lee    <NA>              President
# 1        B0-2  Edward Rodriguez    B0-1       VP of Operations
# 2        B0-3      Maria Cortez    B0-1          VP of Finance
# 3        B0-4     Thomas Nguyen    B0-1       VP of Technology
# 4        B0-5        Rachel Kim    B0-2     Operations Manager
# 5        B0-6     Jeffrey Patel    B0-2      Supply Chain Lead
# 6        B0-7      Olivia Smith    B0-2  Facilities Supervisor
# 7        B0-8      Brian Carter    B0-3     Accounting Manager
# 8        B0-9   Lauren Anderson    B0-3      Financial Analyst
# 9       B0-10   Santiago Romero    B0-3     Payroll Specialist

7. Enrich existing data with additional columns

from mostlyai import mock
import pandas as pd

tables = {
    "guests": {
        "prompt": "Guests of an Alpine ski hotel in Austria",
        "columns": {
            "gender": {"dtype": "category", "values": ["male", "female"]},
            "age": {"prompt": "age in years; min: 18, max: 80; avg: 25", "dtype": "integer"},
            "room_number": {"prompt": "room number", "dtype": "integer"},
            "is_vip": {"prompt": "is the guest a VIP", "dtype": "boolean"},
        },
        "primary_key": "guest_id",
    }
}
existing_guests = pd.DataFrame({
    "guest_id": [1, 2, 3],
    "name": ["Anna Schmidt", "Marco Rossi", "Sophie Dupont"],
    "nationality": ["DE", "IT", "FR"],
})
df = mock.sample(
    tables=tables,
    existing_data={"guests": existing_guests},
    model="openai/gpt-4.1-nano"
)
print(df)
#   guest_id           name nationality  gender  age  room_number is_vip
# 0        1   Anna Schmidt          DE  female   30          102  False
# 1        2    Marco Rossi          IT    male   27          215   True
# 2        3  Sophie Dupont          FR  female   22          108  False

MCP Server

This repo comes with MCP Server. It can be easily consumed by any MCP Client by providing the following configuration:

{
  "mcpServers": {
      "mostlyai-mock-mcp": {
          "command": "uvx",
          "args": ["--from", "mostlyai-mock", "mcp-server"],
          "env": {
              "OPENAI_API_KEY": "PROVIDE YOUR KEY",
              "GEMINI_API_KEY": "PROVIDE YOUR KEY",
              "GROQ_API_KEY": "PROVIDE YOUR KEY",
              "ANTHROPIC_API_KEY": "PROVIDE YOUR KEY"
          }
      }
  }
}

For example:

• in Claude Desktop, go to "Settings" > "Developer" > "Edit Config" and paste the above into claude_desktop_config.json
• in Cursor, go to "Settings" > "Cursor Settings" > "MCP" > "Add new global MCP server" and paste the above into mcp.json

Troubleshooting:

1. If the MCP Client fails to detect the MCP Server, provide the absolute path in the command field, for example: /Users/johnsmith/.local/bin/uvx
2. To debug MCP Server issues, you can use MCP Inspector by running: npx @modelcontextprotocol/inspector -- uvx --from mostlyai-mock mcp-server
3. In order to develop locally, modify the configuration by replacing "command": "uv" (or use the full path to uv if needed) and "args": ["--directory", "/Users/johnsmith/mostlyai-mock", "run", "mcp-server"]