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