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

Update Version:
- Increment the version number in pyproject.toml and src/__init__.py.
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.
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

Install Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Tools

Production-ready MCP server that provides LLMs with essential random generation abilities, including random integers, floats, choices, shuffling, and cryptographically secure tokens.

Related Resources

Reddit Discussion about this server

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