MCP server
by turlockmike
Random Generation offers diverse tools like random number generator, rng generator, random counter, and more for all you
Generates random data including UUIDs, numbers, passwords, strings, and simulates dice rolls and card draws. All generation happens locally without external API calls.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
Random Generation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Random Generation reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Strong directory entry: Random Generation surfaces stars and publisher context so we could sanity-check maintenance before adopting.
According to our notes, Random Generation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired Random Generation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Useful MCP listing: Random Generation is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, Random Generation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: Random Generation is the kind of server we cite when onboarding engineers to host + tool permissions.
Random Generation is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
I recommend Random Generation for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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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>
npm install mcp-rand
Or install globally:
npm install -g mcp-rand
npx mcp-rand
Add to your MCP client configuration:
{
"mcpServers": {
"mcp-rand": {
"command": "node",
"args": ["path/to/mcp-rand/build/index.js"],
"disabled": false,
"alwaysAllow": []
}
}
}
// 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=="
}
*/
Please see CONTRIBUTING.md for development setup and guidelines.
ISC
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.