Sample Data MCP

A Model Context Protocol (MCP) server that generates fixed-length test data based on field specifications. This tool helps developers create realistic test datasets with customizable field types and formats.

Features

• Generate fixed-length test data records

• Support for multiple field types:

  • string: Random names using Faker library

  • enum: Random selection from provided values

  • integer: Random integers within specified range

  • date: Random dates with customizable format

  • filler: Space padding fields

• Configurable field length and constraints

• MCP-compatible for integration with Claude Code

Installation

Prerequisites

• Python 3.13 or higher

• UV package manager

Install with UV

1. Clone or download this project

2. Navigate to the project directory

3. Install dependencies:

   uv sync

Install into Claude Code

1. Add the MCP server to your Claude Code configuration. Edit your MCP settings file (typically ~/.config/claude-code/mcp_servers.json or similar):

   {
     "mcpServers": {
       "sample-data-mcp": {
         "command": "uv",
         "args": ["run", "/path/to/sample-data-mcp/main.py"],
         "cwd": "/path/to/sample-data-mcp"
       }
     }
   }

2. Restart Claude Code to load the new MCP server

Usage

Once installed, you can use the generate_test_data_tool through Claude Code to create test data:

Example Field Specification

fields = [
    {
        "name": "customer_id",
        "type": "integer",
        "length": 8,
        "min": 1000,
        "max": 9999
    },
    {
        "name": "customer_name",
        "type": "string",
        "length": 25
    },
    {
        "name": "status",
        "type": "enum",
        "length": 6,
        "values": ["ACTIVE", "INACTIVE", "PENDING"]
    },
    {
        "name": "signup_date",
        "type": "date",
        "length": 8,
        "format": "%Y%m%d"
    },
    {
        "name": "filler",
        "type": "filler",
        "length": 5
    }
]

Field Types

Development

Testing Locally

uv run mcp dev main.py

Dependencies

• faker: For generating realistic fake data

• mcp: Model Context Protocol implementation

• pydantic: Data validation and settings management

License

This project is provided as-is for educational and development purposes.