Random Number

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.

Looking for the agent skill version?random-number-skillsimplements the same random number generation strategy as an agent skill instead of an MCP server.

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

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

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

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

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

Generate a random float between low and high.

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

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

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

- 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)

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

Return a new list with items in random order.

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

Choose k unique items from population without replacement.

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

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

Generate a cryptographically secure random hex token.

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

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

Generate a cryptographically secure random integer below upper_bound.

- upper_bound(int): Upper bound (exclusive)

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

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'srandommodule - fast but not cryptographically secure
- Secure functions(secure_token_hex,secure_random_int): Use Python'ssecretsmodule - slower but cryptographically secure

# 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/

{ "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-mcpwith the absolute path to your cloned repository.

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

- Increment theversionnumber inpyproject.toml,src/random_number_mcp/__init__.py, andserver.json.

-

Add a new entry inCHANGELOG.mdfor the release.

- Draft notes with coding agent usinggit diffcontext.

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.

- Draft a new release on the GitHub UI.

- Tag release using UI.

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

mcp-name: io.github.zazencodes/random-number-mcp

MIT License - seeLICENSEfile for details.

Analyze large codebases and document collections using high-context models via OpenRouter, OpenAI, or Google AI -- very useful, e.g., with Claude Code