random-number-mcp 0.1.0

Random-number utilities exposed via Model Context Protocol

Random Number MCP

Production-ready MCP server that provides LLMs with essential random generation abilities --- built entirely on Python's standard library.

🎲 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()
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]
  }
}

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
uv run ruff format

# Type checking
uv run mypy src/

Building

# Build package
uv build

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

Notes

• The server communicates via STDIO using JSON-RPC 2.0 protocol.

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