Which integrations are available for this server?

Enables deploying the MCP server from a forked repository through Smithery integration. Utilizes Node.js crypto module for cryptographically secure random number generation, with compatibility for Node.js 18.0.0 or higher. Available as an npm package for easy installation and integration with MCP clients. Written in TypeScript with comprehensive type definitions for type safety. Supports installation via yarn package manager as an alternative to npm.

How do I use Pluggedin Random Number Generator?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Pluggedin Random Number Generator generate a random integer between 1 and 100" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Pluggedin Random Number Generator

by VeriTeknik

Overview Schema Related Servers Score Discussions

JavaScript

Hybrid

Plugged.in Random Number Generator MCP Server

A state-of-the-art cryptographically secure random number generator server implementing the Model Context Protocol (MCP). This server provides advanced random number generation capabilities for AI applications, LLMs, and other systems requiring high-quality randomness.

🚀 Features

Cryptographically Secure: Uses Node.js built-in crypto module for cryptographically secure pseudorandom number generation (CSPRNG)
Multiple Data Types: Generate integers, floats, bytes, UUIDs, strings, booleans, and random choices
Flexible Configuration: Customizable ranges, counts, encodings, and character sets
MCP Compliant: Full compatibility with Model Context Protocol specification including tools and prompts
AI-Friendly Prompts: Built-in prompt to help LLMs understand they should use this server for random generation
Type Safety: Written in TypeScript with comprehensive type definitions
Error Handling: Robust input validation and error reporting
Performance Optimized: Efficient algorithms suitable for high-throughput applications

Related MCP server: Random Number MCP

📦 Installation

Prerequisites

Node.js 18.0.0 or higher
npm or yarn package manager

Install via Desktop Extension (DXT)

For Claude Desktop users, you can install this server as a one-click Desktop Extension:

Download the latest .dxt file from the releases page
Open Claude Desktop
Go to Settings → Developer → MCP Servers
Click "Install from file" and select the downloaded .dxt file

Install from npm

npm install -g pluggedin-random-number-generator-mcp

Or install locally in your project:

npm install pluggedin-random-number-generator-mcp

Deploy with Smithery

Deploy this MCP server to the cloud using Smithery:

Fork this repository
Connect your GitHub account to Smithery
Navigate to the Deployments tab
Click "Deploy"

The server includes a smithery.yaml configuration file for easy deployment.

Build from Source

git clone https://github.com/VeriTeknik/pluggedin-random-number-generator-mcp.git
cd pluggedin-random-number-generator-mcp
npm install
npm run build

# Optional: Build DXT package
npm run build:dxt

🛠️ Usage

Running the Server

The server communicates via stdio (standard input/output) following the MCP protocol:

# Using the built version
node dist/index.js

# Using development mode
npm run dev

Integration with MCP Clients

For npm installation (recommended):

Add to your MCP client configuration. For Claude Desktop, add to your claude_desktop_config.json:

{
  "mcpServers": {
    "random-generator": {
      "command": "npx",
      "args": ["-y", "pluggedin-random-number-generator-mcp@latest"]
    }
  }
}

This will always use the latest version from npm without requiring a global installation.

For local installation:

{
  "mcpServers": {
    "random-generator": {
      "command": "node",
      "args": ["node_modules/pluggedin-random-number-generator-mcp/dist/index.js"]
    }
  }
}

🔧 Available Tools

1. Generate Random Integers

Generate cryptographically secure random integers within a specified range.

Parameters:

min (integer, optional): Minimum value (inclusive), default: 0
max (integer, optional): Maximum value (inclusive), default: 100
count (integer, optional): Number of integers to generate, default: 1, max: 1000

Example:

{
  "name": "generate_random_integer",
  "arguments": {
    "min": 1,
    "max": 100,
    "count": 5
  }
}

2. Generate Random Floats

Generate cryptographically secure random floating-point numbers.

Parameters:

min (number, optional): Minimum value (inclusive), default: 0.0
max (number, optional): Maximum value (exclusive), default: 1.0
count (integer, optional): Number of floats to generate, default: 1, max: 1000
precision (integer, optional): Decimal places to round to, default: 6, max: 15

Example:

{
  "name": "generate_random_float", 
  "arguments": {
    "min": 0.0,
    "max": 1.0,
    "count": 3,
    "precision": 4
  }
}

3. Generate Random Bytes

Generate cryptographically secure random bytes in various encodings.

Parameters:

length (integer, optional): Number of bytes to generate, default: 32, max: 1024
encoding (string, optional): Output encoding ("hex", "base64", "binary"), default: "hex"

Example:

{
  "name": "generate_random_bytes",
  "arguments": {
    "length": 32,
    "encoding": "hex"
  }
}

4. Generate UUIDs

Generate cryptographically secure UUID version 4 identifiers.

Parameters:

count (integer, optional): Number of UUIDs to generate, default: 1, max: 100
format (string, optional): UUID format ("standard", "compact"), default: "standard"

Example:

{
  "name": "generate_uuid",
  "arguments": {
    "count": 3,
    "format": "standard"
  }
}

5. Generate Random Strings

Generate cryptographically secure random strings with customizable character sets.

Parameters:

length (integer, optional): String length, default: 16, max: 256
charset (string, optional): Character set ("alphanumeric", "alphabetic", "numeric", "hex", "base64", "ascii_printable"), default: "alphanumeric"
count (integer, optional): Number of strings to generate, default: 1, max: 100

Example:

{
  "name": "generate_random_string",
  "arguments": {
    "length": 12,
    "charset": "alphanumeric",
    "count": 2
  }
}

6. Generate Random Choices

Randomly select items from a provided list using cryptographically secure randomness.

Parameters:

choices (array, required): Array of string items to choose from
count (integer, optional): Number of items to select, default: 1
allow_duplicates (boolean, optional): Whether to allow duplicate selections, default: true

Example:

{
  "name": "generate_random_choice",
  "arguments": {
    "choices": ["apple", "banana", "cherry", "date"],
    "count": 2,
    "allow_duplicates": false
  }
}

7. Generate Random Booleans

Generate cryptographically secure random boolean values with configurable probability.

Parameters:

count (integer, optional): Number of booleans to generate, default: 1, max: 1000
probability (number, optional): Probability of true (0.0 to 1.0), default: 0.5

Example:

{
  "name": "generate_random_boolean",
  "arguments": {
    "count": 10,
    "probability": 0.7
  }
}

🤖 AI Prompts

The server includes a built-in prompt to help LLMs understand they should use this server for random number generation rather than attempting to generate random values themselves.

Available Prompt: `generate_random`

This prompt educates the AI about its limitations in generating random numbers and guides it to use the available cryptographically secure tools.

Parameters:

type (string, optional): Type of random value needed (integer, float, uuid, string, bytes, choice, boolean)
requirements (string, optional): Specific requirements for the random generation

Example Usage: When an LLM receives a request like "Generate a random password" or "Pick a random number", the prompt will:

Acknowledge that LLMs cannot generate truly random values
Explain the available cryptographically secure tools
Guide the AI to use the appropriate tool for the task

This ensures that all random generation in your application uses proper cryptographic methods rather than predictable AI-generated patterns.

🔒 Security Features

This server implements several security best practices:

Cryptographically Secure Randomness: All random number generation uses Node.js crypto module functions (randomBytes, randomInt, randomUUID) which provide cryptographically secure pseudorandom numbers suitable for security-sensitive applications.
Input Validation: Comprehensive validation of all input parameters to prevent injection attacks and ensure data integrity.
Rate Limiting: Built-in limits on generation counts to prevent resource exhaustion attacks.
Error Handling: Secure error messages that don't leak sensitive information about the system state.

🧪 Testing

The server includes a comprehensive test suite that validates all functionality:

# Run the test suite
node test.js

The test suite covers:

Tool discovery and listing
All random generation functions
Input validation and error handling
Output format verification
Statistical properties validation

📊 Performance

The server is optimized for performance while maintaining security:

Efficient Algorithms: Uses optimized native crypto functions
Memory Management: Minimal memory footprint with efficient buffer handling
Concurrent Requests: Thread-safe design supporting multiple simultaneous requests
Scalability: Suitable for high-throughput applications

🔧 Development

Project Structure

pluggedin-random-number-generator-mcp/
├── src/
│   └── index.ts          # Main server implementation
├── dist/                 # Compiled JavaScript output
├── test.js              # Comprehensive test suite
├── package.json         # Project configuration
├── tsconfig.json        # TypeScript configuration
└── README.md           # This documentation

Building

npm run build

Development Mode

npm run dev

Testing with MCP Inspector

You can test the server using the MCP Inspector tool:

npm run inspector

This will start the MCP Inspector web interface where you can:

View available tools
Test tool execution
Inspect request/response payloads
Debug server behavior

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Guidelines

Follow TypeScript best practices
Maintain comprehensive test coverage
Update documentation for new features
Ensure all tests pass before submitting
Follow semantic versioning for releases

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Model Context Protocol - The official MCP specification
Plugged.in - MCP server management and discovery platform
MCP SDK - Official TypeScript SDK for MCP

📞 Support

For support, questions, or feature requests:

Open an issue on GitHub
Visit the Plugged.in platform for MCP server management
Check the MCP documentation for protocol details

Available Tools

7 tools

generate_random_booleanB

Generate cryptographically secure random boolean values

ParametersJSON Schema

Name	Required	Description	Default
`count`	No	Number of random booleans to generate
`probability`	No	Probability of true (0.0 to 1.0)

TDQS

B3.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds value by specifying 'cryptographically secure', which implies high randomness quality and security considerations beyond basic generation. However, it doesn't detail output format, error handling, or performance traits like rate limits, leaving gaps in behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes essential information, earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (simple generation with two well-documented parameters) and no output schema, the description is minimally adequate. It covers the basic purpose and security aspect but lacks details on output format or usage scenarios. With no annotations, it should do more to compensate, but the simplicity keeps it from being severely incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents both parameters (count and probability). The description adds no parameter-specific information beyond what the schema provides, such as examples or edge cases. Baseline 3 is appropriate as the schema handles the heavy lifting without extra value from the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'generate' and the resource 'cryptographically secure random boolean values', making the purpose specific and understandable. It distinguishes from siblings by specifying 'boolean' values rather than bytes, choices, floats, etc., though it doesn't explicitly contrast with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like generate_random_choice or generate_random_integer for boolean-like outcomes. It lacks context about typical use cases or prerequisites, offering only a basic functional statement without comparative or exclusionary advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_random_bytesC

Generate cryptographically secure random bytes

ParametersJSON Schema

Name	Required	Description	Default
`encoding`	No	Output encoding format	hex
`length`	No	Number of random bytes to generate

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'cryptographically secure,' which hints at quality and safety, but doesn't address potential side effects, rate limits, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded and appropriately sized, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like security implications, performance, or return format, which are crucial for a tool generating random data. The minimal information provided is insufficient for full contextual understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds no parameter-specific information beyond what the schema already provides (100% coverage). It doesn't explain the semantics of 'length' or 'encoding' in the context of random bytes generation. Since the schema fully documents parameters, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('generate') and resource ('cryptographically secure random bytes'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like generate_random_string or generate_random_integer, which would require a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like generate_random_string or generate_random_integer. It lacks context about scenarios where raw bytes are preferred over other random data types, offering no usage boundaries or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_random_choiceA

Randomly select items from a given list using cryptographically secure randomness

ParametersJSON Schema

Name	Required	Description
`allow_duplicates`	No	Whether to allow duplicate selections
`choices`	Yes	Array of items to choose from
`count`	No	Number of items to select

TDQS

A3.9/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context by specifying 'cryptographically secure randomness,' which informs the agent about the quality and security of the randomness, though it lacks details on error handling, performance, or output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that is front-loaded with the core functionality and includes a key behavioral detail ('cryptographically secure randomness') without any wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is somewhat complete but lacks details on output format, error cases, or practical examples, which could help the agent use it more effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (choices, count, allow_duplicates). The description does not add any meaning beyond what the schema provides, such as explaining interactions between parameters or usage examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('select') and resource ('items from a given list'), and it distinguishes itself from siblings by focusing on list-based random selection rather than generating specific data types like booleans, bytes, floats, integers, strings, or UUIDs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for random selection from lists, but it does not explicitly state when to use this tool versus alternatives like generate_random_string or generate_random_integer, nor does it provide exclusions or prerequisites for use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_random_floatB

Generate cryptographically secure random floating-point numbers

ParametersJSON Schema

Name	Required	Description
`count`	No	Number of random floats to generate
`max`	No	Maximum value (exclusive)
`min`	No	Minimum value (inclusive)
`precision`	No	Number of decimal places to round to

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds value by specifying 'cryptographically secure' (implying high-quality randomness suitable for security applications) and 'floating-point numbers' (indicating decimal outputs). However, it lacks details on performance, error handling, or output format (e.g., array vs. single value), which are critical for a tool with parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste—it directly states the tool's core function without redundancy. It's appropriately sized for a straightforward tool and front-loaded with essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and security aspect but omits guidance on usage versus siblings and behavioral details like output structure. Without annotations or output schema, more context on what to expect (e.g., returns a list) would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all four parameters (count, min, max, precision) with descriptions, defaults, and constraints. The description adds no parameter-specific information beyond implying floating-point outputs, aligning with the baseline score of 3 when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('generate') and resource ('cryptographically secure random floating-point numbers'), making the purpose immediately understandable. It distinguishes from siblings by specifying 'floating-point numbers' rather than booleans, bytes, integers, etc., though it doesn't explicitly contrast with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings (e.g., generate_random_integer for whole numbers, generate_random_choice for selections). There's no mention of use cases, prerequisites, or alternatives, leaving the agent to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_random_integerA

Generate cryptographically secure random integers within a specified range

ParametersJSON Schema

Name	Required	Description
`count`	No	Number of random integers to generate
`max`	No	Maximum value (inclusive)
`min`	No	Minimum value (inclusive)

TDQS

A3.5/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It adds valuable context by specifying 'cryptographically secure' (implying high-quality randomness suitable for security applications) and 'within a specified range' (defining the output scope). However, it doesn't disclose rate limits, error conditions, or performance characteristics that would be helpful for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core functionality ('Generate cryptographically secure random integers') and adds essential qualification ('within a specified range'). Every word earns its place with zero waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the core purpose and security aspect but lacks details on output format (e.g., array of integers), error handling, or sibling differentiation that would make it more complete for agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters (count, min, max) with their descriptions, defaults, and constraints. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('generate') and resource ('cryptographically secure random integers') with specific scope ('within a specified range'). It distinguishes from sibling tools like generate_random_boolean, generate_random_bytes, etc., by specifying integer generation rather than other data types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like generate_random_float or generate_random_choice. It lacks explicit context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_random_stringB

Generate a cryptographically secure random string

ParametersJSON Schema

Name	Required	Description	Default
`charset`	No	Character set to use	alphanumeric
`count`	No	Number of random strings to generate
`length`	No	Length of the random string

TDQS

B3.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It specifies 'cryptographically secure,' which adds important context about security properties beyond basic randomness. However, it doesn't mention performance, rate limits, or error conditions, leaving gaps for a tool that generates secure data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes a key behavioral detail ('cryptographically secure'), making it appropriately sized and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally complete. It covers the core purpose and security aspect but lacks usage guidance, error handling, or output format details, which could help an agent use it more effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with clear documentation for charset, count, and length. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for adequate but not enhanced parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('generate') and resource ('cryptographically secure random string'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like generate_random_bytes or generate_random_choice, which also generate random data but with different outputs or constraints.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like generate_random_bytes (for raw bytes) or generate_random_choice (for selecting from a list). It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_uuidA

Generate a cryptographically secure UUID (v4)

ParametersJSON Schema

Name	Required	Description	Default
`count`	No	Number of UUIDs to generate
`format`	No	UUID format	standard

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the cryptographic security trait, which is valuable behavioral context. However, it doesn't mention performance aspects, error conditions, or what the output looks like (e.g., string format), leaving gaps in behavioral understanding.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes only essential qualifiers (cryptographically secure, v4). Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (simple generation function), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers the core purpose but lacks details on output format, error handling, or security guarantees, which could be helpful for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents both parameters (count and format). The description adds no parameter-specific information beyond what the schema provides, such as explaining the practical difference between 'standard' and 'compact' formats. Baseline 3 is appropriate when schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Generate') and resource ('cryptographically secure UUID (v4)'), distinguishing it from sibling tools like generate_random_string or generate_random_integer. It precisely identifies the type of UUID (v4) and its security property.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for generating UUIDs but provides no explicit guidance on when to use this tool versus alternatives like generate_random_string for other random data needs. It lacks any when-not-to-use or prerequisite information, leaving usage context to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose targeting different types of random data generation: boolean, bytes, choice selection, float, integer, string, and UUID. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tools follow a consistent 'generate_random_' prefix pattern (except 'generate_uuid' which logically fits as a special case), with snake_case used uniformly. This predictable naming scheme makes the tool set easy to navigate and understand.

Tool Count5/5

With 7 tools, the server is well-scoped for random data generation, covering common use cases without being overwhelming. Each tool earns its place by addressing a specific type of random output, making the count appropriate for the domain.

Completeness5/5

The tool set provides complete coverage for random data generation, including boolean, numeric (integer and float), string, bytes, selection from lists, and UUIDs. There are no obvious gaps; agents can handle a wide range of randomization tasks without dead ends.

Maintenance

ActivityInactive

ResponsivenessSyncing

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Plugged.in Random Number Generator MCP Server

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🔧 Available Tools

1. Generate Random Integers

2. Generate Random Floats

3. Generate Random Bytes

4. Generate UUIDs

5. Generate Random Strings

6. Generate Random Choices

7. Generate Random Booleans

🤖 AI Prompts

Available Prompt: generate_random

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Available Prompt: `generate_random`