Generate Secure Random Numbers and Tokens
Generate random integers, floats, weighted choices, shuffles, and cryptographically secure tokens - built on Python's stdlib.
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Why it matters
Leverage Python's standard library for robust random number generation, including pseudorandom and cryptographically secure options for integers, floats, sampling, and token creation.
Outcomes
What it gets done
Generate pseudorandom integers and floats.
Create cryptographically secure random integers and hex tokens.
Perform weighted sampling, list shuffling, and unique element selection.
Integrate seamlessly with Python projects for diverse applications.
Source
Get it from source
Spark does not host a copy of it.
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Capabilities
Tools your agent gets
Generate random integers within a specified range
Generate random floating-point numbers within a specified range
Select elements from a list with optional weights for weighted sampling
Return a new list with shuffled elements in random order
Select k unique elements from a population without replacement
Generate cryptographically secure hexadecimal tokens
Generate cryptographically secure random integers
Overview
Random Number MCP Server
Random Number MCP Server provides Python stdlib-based random generation - pseudorandom integers, floats, weighted choices, and shuffles via the random module, plus cryptographically secure tokens and integers via the secrets module. Use pseudorandom tools for simulations and games; use secure_token_hex/secure_random_int instead whenever the output needs to resist prediction, such as tokens or codes.
What it does
Random Number MCP Server provides random number generation utilities from Python's standard library, covering both fast pseudorandom operations (integers, floats, weighted sampling, list shuffling) and cryptographically secure operations (secure tokens, secure random integers).
When to use - and when NOT to
Use pseudorandom tools (random_int, random_float, random_choices, random_shuffle, random_sample) for simulations, games, or any non-security-sensitive randomness need - they're fast but not cryptographically safe. Use the secure tools (secure_token_hex, secure_random_int) instead whenever the output needs to resist prediction - session tokens, password reset codes, or other security-critical values - since they're built on Python's secrets module rather than random. Do not use the pseudorandom tools for anything security-sensitive; they explicitly are not cryptographically secure.
Capabilities
random_int: generate a random integer within a range.random_float: generate a random floating-point number within a range.random_choices: pick items from a list, with optional weights.random_shuffle: return a new list with elements shuffled.random_sample: pick k unique elements from a population without replacement.secure_token_hex: generate a cryptographically secure hex token.secure_random_int: generate a cryptographically secure random integer.
How to install
uvx random-number-mcp
Configure Claude Desktop:
{
"mcpServers": {
"random-number": {
"command": "uvx",
"args": ["random-number-mcp"]
}
}
}
For local development: git clone https://github.com/example/random-number-mcp && cd random-number-mcp && uv sync --dev, then debug with npx @modelcontextprotocol/inspector uv run random-number-mcp. Requires Python 3.10+ and the uv package manager.
Who it's for
Developers who need an AI assistant to generate random values for simulations, games, sampling, or security-sensitive tokens, choosing the right tool (pseudorandom vs. cryptographically secure) for the use case.
Source README
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.
Looking for the agent skill version? random-number-skills implements the same random number generation strategy as an agent skill instead of an MCP server.
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 fromk(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 fromk(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'srandommodule - fast but not cryptographically secure - Secure functions (
secure_token_hex,secure_random_int): Use Python'ssecretsmodule - 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
versionnumber inpyproject.toml,src/random_number_mcp/__init__.py, andserver.json.
- Increment the
Update Changelog:
Add a new entry in
CHANGELOG.mdfor the release.- Draft notes with coding agent using
git 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]- Draft notes with coding agent using
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.
- Draft a new release on the GitHub UI.
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
MCP Registry
mcp-name: io.github.zazencodes/random-number-mcp
FAQ
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Discussion
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