Random Generation▌
by turlockmike
Random Generation offers diverse tools like random number generator, rng generator, random counter, and more for all you
Provides diverse random generation utilities including UUIDs, numbers, strings, passwords, and more.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers needing test data and identifiers
- / Creating secure passwords and tokens
- / Gaming applications requiring dice rolls
- / Mock data generation for testing
capabilities
- / Generate UUID v4 identifiers
- / Generate random numbers with custom ranges
- / Create secure passwords with mixed character types
- / Generate random strings with configurable character sets
- / Roll dice using standard notation (2d6, 1d20, etc.)
- / Draw cards from a standard playing deck
what it does
Generates random data including UUIDs, numbers, passwords, strings, and simulates dice rolls and card draws. All generation happens locally without external API calls.
about
Random Generation is a community-built MCP server published by turlockmike that provides AI assistants with tools and capabilities via the Model Context Protocol. Random Generation offers diverse tools like random number generator, rng generator, random counter, and more for all you It is categorized under developer tools. This server exposes 7 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install Random Generation in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Random Generation is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
MCP Rand
A Model Context Protocol (MCP) server providing various random generation utilities, including UUID, numbers, strings, passwords, Gaussian distribution, dice rolling, and card drawing.
<a href="https://glama.ai/mcp/servers/ccd6b0hni8"><img width="380" height="200" src="https://glama.ai/mcp/servers/ccd6b0hni8/badge" alt="Rand MCP server" /></a>
Installation
npm install mcp-rand
Or install globally:
npm install -g mcp-rand
Features
UUID Generator
- Generates RFC 4122 version 4 UUIDs
- Uses Node's native crypto module for secure random generation
- No parameters required
Random Number Generator
- Generates random numbers within a specified range
- Configurable minimum and maximum values (inclusive)
- Defaults to range 0-100 if no parameters provided
Gaussian Random Generator
- Generates random numbers following a Gaussian (normal) distribution
- Normalized to range 0-1
- No parameters required
Random String Generator
- Generates random strings with configurable length and character sets
- Supports multiple character sets:
- alphanumeric (default): A-Z, a-z, 0-9
- numeric: 0-9
- lowercase: a-z
- uppercase: A-Z
- special: !@#$%^&*()_+-=[]{};'"|,.<>/?
- Configurable string length (defaults to 10)
Password Generator
- Generates strong passwords with a mix of character types
- Ensures at least one character from each type (uppercase, lowercase, numbers, special)
- Configurable length (minimum 8, default 16)
- WARNING: While passwords are generated locally, it's recommended to use a dedicated password manager
Dice Roller
- Roll multiple dice using standard dice notation
- Supports notation like "2d6" (two six-sided dice), "1d20" (one twenty-sided die)
- Returns individual rolls and total for each set of dice
- Can roll multiple different dice sets at once (e.g., "2d6", "1d20", "4d4")
Card Drawer
- Draw cards from a standard 52-card deck
- Maintains deck state between draws using base64 encoding
- Returns drawn cards and remaining deck state
- Supports drawing any number of cards up to the deck size
- Properly shuffles available cards before each draw
Usage
As a CLI Tool
npx mcp-rand
Integration with MCP Clients
Add to your MCP client configuration:
{
"mcpServers": {
"mcp-rand": {
"command": "node",
"args": ["path/to/mcp-rand/build/index.js"],
"disabled": false,
"alwaysAllow": []
}
}
}
Example Usage
// Generate UUID
const uuid = await client.callTool('generate_uuid', {});
console.log(uuid); // e.g., "550e8400-e29b-41d4-a716-446655440000"
// Generate random number
const number = await client.callTool('generate_random_number', {
min: 1,
max: 100
});
console.log(number); // e.g., 42
// Generate Gaussian random number
const gaussian = await client.callTool('generate_gaussian', {});
console.log(gaussian); // e.g., 0.6827
// Generate random string
const string = await client.callTool('generate_string', {
length: 15,
charset: 'alphanumeric'
});
console.log(string); // e.g., "aB9cD8eF7gH6iJ5"
// Generate password
const password = await client.callTool('generate_password', {
length: 20
});
console.log(password); // e.g., "aB9#cD8$eF7@gH6*iJ5"
// Roll dice
const rolls = await client.callTool('roll_dice', {
dice: ['2d6', '1d20', '4d4']
});
console.log(rolls);
/* Output example:
[
{
"dice": "2d6",
"rolls": [3, 1],
"total": 4
},
{
"dice": "1d20",
"rolls": [4],
"total": 4
},
{
"dice": "4d4",
"rolls": [2, 3, 2, 3],
"total": 10
}
]
*/
// Draw cards
const draw1 = await client.callTool('draw_cards', {
count: 5
});
console.log(draw1);
/* Output example:
{
"drawnCards": [
{ "suit": "hearts", "value": "A" },
{ "suit": "diamonds", "value": "7" },
{ "suit": "clubs", "value": "K" },
{ "suit": "spades", "value": "2" },
{ "suit": "hearts", "value": "10" }
],
"remainingCount": 47,
"deckState": "t//+///bDw=="
}
*/
// Draw more cards using previous deck state
const draw2 = await client.callTool('draw_cards', {
count: 3,
deckState: draw1.deckState
});
console.log(draw2);
/* Output example:
{
"drawnCards": [
{ "suit": "diamonds", "value": "Q" },
{ "suit": "clubs", "value": "5" },
{ "suit": "spades", "value": "J" }
],
"remainingCount": 44,
"deckState": "l//+//zbDw=="
}
*/
Contributing
Please see CONTRIBUTING.md for development setup and guidelines.
License
ISC
FAQ
- What is the Random Generation MCP server?
- Random Generation is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Random Generation?
- This profile displays 62 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.7★★★★★62 reviews- ★★★★★Chaitanya Patil· Dec 28, 2024
Random Generation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Ren Nasser· Dec 28, 2024
Random Generation reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Sakura Haddad· Dec 24, 2024
Strong directory entry: Random Generation surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Nia Ghosh· Dec 20, 2024
According to our notes, Random Generation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★William Thompson· Dec 16, 2024
We wired Random Generation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Pratham Ware· Dec 4, 2024
Useful MCP listing: Random Generation is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Piyush G· Nov 19, 2024
According to our notes, Random Generation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Isabella Li· Nov 19, 2024
Useful MCP listing: Random Generation is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakura Ndlovu· Nov 19, 2024
Random Generation is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Sakura Lopez· Nov 15, 2024
I recommend Random Generation for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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