random-number-mcp 0.1.1

Random-number utilities exposed via Model Context Protocol

Random Number MCP

Essential random number generation utilities from the Python standard library, including pseudorandom and cryptographically secure operations for integers, floats, weighted selections, list shuffling, and secure token generation.

📺 Demo Video

https://github.com/user-attachments/assets/303a441a-2b10-47e3-b2a5-c8b51840e362

🎲 Tools

Tool	Purpose	Python function
random_int	Generate random integers	random.randint()
random_float	Generate random floats	random.uniform()
random_choices	Choose items from a list (optional weights)	random.choices()
random_shuffle	Return a new list with items shuffled	random.sample()
random_sample	Choose k unique items from population	random.sample()
secure_token_hex	Generate cryptographically secure hex tokens	secrets.token_hex()
secure_random_int	Generate cryptographically secure integers	secrets.randbelow()

🔧 Setup

Claude Desktop

Add this to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "random-number": {
      "command": "uvx",
      "args": ["random-number-mcp"]
    }
  }
}

📋 Tool Reference

random_int

Generate a random integer between low and high (inclusive).

Parameters:

• low (int): Lower bound (inclusive)
• high (int): Upper bound (inclusive)

Example:

{
  "name": "random_int",
  "arguments": {
    "low": 1,
    "high": 100
  }
}

random_float

Generate a random float between low and high.

Parameters:

• low (float, optional): Lower bound (default: 0.0)
• high (float, optional): Upper bound (default: 1.0)

Example:

{
  "name": "random_float",
  "arguments": {
    "low": 0.5,
    "high": 2.5
  }
}

random_choices

Choose k items from a population with replacement, optionally weighted.

Parameters:

• population (list): List of items to choose from
• k (int, optional): Number of items to choose (default: 1)
• weights (list, optional): Weights for each item (default: equal weights)

Example:

{
  "name": "random_choices",
  "arguments": {
    "population": ["red", "blue", "green", "yellow"],
    "k": 2,
    "weights": [0.4, 0.3, 0.2, 0.1]
  }
}

random_shuffle

Return a new list with items in random order.

Parameters:

• items (list): List of items to shuffle

Example:

{
  "name": "random_shuffle",
  "arguments": {
    "items": [1, 2, 3, 4, 5]
  }
}

random_sample

Choose k unique items from population without replacement.

Parameters:

• population (list): List of items to choose from
• k (int): Number of items to choose

Example:

{
  "name": "random_sample",
  "arguments": {
    "population": ["a", "b", "c", "d", "e"],
    "k": 2
  }
}

secure_token_hex

Generate a cryptographically secure random hex token.

Parameters:

• nbytes (int, optional): Number of random bytes (default: 32)

Example:

{
  "name": "secure_token_hex",
  "arguments": {
    "nbytes": 16
  }
}

secure_random_int

Generate a cryptographically secure random integer below upper_bound.

Parameters:

• upper_bound (int): Upper bound (exclusive)

Example:

{
  "name": "secure_random_int",
  "arguments": {
    "upper_bound": 1000
  }
}

🔒 Security Considerations

This package provides both standard pseudorandom functions (suitable for simulations, games, etc.) and cryptographically secure functions (suitable for tokens, keys, etc.):

• Standard functions (random_int, random_float, random_choices, random_shuffle): Use Python's random module - fast but not cryptographically secure
• Secure functions (secure_token_hex, secure_random_int): Use Python's secrets module - slower but cryptographically secure

🛠️ Development

Prerequisites

• Python 3.10+
• uv package manager

Setup

# Clone the repository
git clone https://github.com/example/random-number-mcp
cd random-number-mcp

# Install dependencies
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check --fix
uv run ruff format

# Type checking
uv run mypy src/

MCP Client Config

{
  "mcpServers": {
    "random-number-dev": {
      "command": "uv",
      "args": [
        "--directory",
        "<path_to_your_repo>/random-number-mcp",
        "run",
        "random-number-mcp"
      ]
    }
  }
}

Note: Replace <path_to_your_repo>/random-number-mcp with the absolute path to your cloned repository.

Building

# Build package
uv build

# Test installation
uv run --with dist/*.whl random-number-mcp

Release Checklist

1. Update Version:

   • Increment the version number in pyproject.toml and src/__init__.py.

2. Update Changelog:

   • Add a new entry in CHANGELOG.md for the release.

     • Draft notes with coding agent using git diff context.

     Update the @CHANGELOG.md for the latest release.
     List all significant changes, bug fixes, and new features.
     Here's the git diff:
     [GIT_DIFF]
     

   • Commit along with any other pending changes.

3. Create GitHub Release:

   • Draft a new release on the GitHub UI.
     • Tag release using UI.
   • The GitHub workflow will automatically build and publish the package to PyPI.

Testing with MCP Inspector

For exploring and/or developing this server, use the MCP Inspector npm utility:

# Install MCP Inspector
npm install -g @modelcontextprotocol/inspector

# Run local development server with the inspector
npx @modelcontextprotocol/inspector uv run random-number-mcp

# Run PyPI production server with the inspector
npx @modelcontextprotocol/inspector uvx random-number-mcp

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📚 Links

• Model Context Protocol
• FastMCP Documentation
• Python random module
• Python secrets module