Which integrations are available for this server?

Supports configuration through environment variables stored in .env files for API keys and service parameters

How do I use Random.org MCP Server?

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., "@Random.org MCP Server generate 5 random numbers 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.

Random.org MCP Server

by QianJue-CN

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Random.org MCP Server

A Model Context Protocol (MCP) server that provides access to the api.random.org service for generating true random numbers, strings, UUIDs, and more.

Features

This MCP server provides the following tools:

generateIntegers - Generate true random integers within a specified range
generateIntegerSequences - Generate sequences of true random integers
generateDecimalFractions - Generate random decimal fractions between 0 and 1
generateGaussians - Generate random numbers from a Gaussian distribution
generateStrings - Generate random strings from specified characters
generateUUIDs - Generate true random UUIDs (version 4)
generateBlobs - Generate random binary data in base64 or hex format
getUsage - Get API usage statistics

Related MCP server: User Management MCP Server

Installation & Deployment

🚀 Quick Start with npm (Recommended)

Method 1: Global Installation

# Install globally
npm install -g random-org-mcp-server

# Verify installation
random-org-mcp --version

Method 2: Use without Installation

# Run directly with npx (no installation required)
npx random-org-mcp-server

Method 3: Local Project Installation

# Install in your project
npm install random-org-mcp-server

# Run from node_modules
npx random-org-mcp-server

🛠️ Build from Source

Clone this repository:

git clone https://github.com/QianJue-CN/TRUERandomMCP.git
cd TRUERandomMCP

Install dependencies:

npm install

Build the project:

npm run build

Configuration

🔑 Get API Key

Visit api.random.org to get a free API key
Register and obtain your API key

⚙️ Configuration Methods

Method 1: Environment Variables (Recommended)

Create a .env file in your working directory:

# Copy example file (if building from source)
cp .env.example .env

Edit .env file:

RANDOM_ORG_API_KEY=your_api_key_here
RATE_LIMIT_REQUESTS_PER_SECOND=1
RATE_LIMIT_BURST_SIZE=5
REQUEST_TIMEOUT_MS=10000
MAX_RETRIES=3
RETRY_DELAY_MS=1000

Method 2: MCP Client Configuration

Configure directly in your MCP client (e.g., Claude Desktop):

{
  "mcpServers": {
    "random-org": {
      "command": "npx",
      "args": ["random-org-mcp-server"],
      "env": {
        "RANDOM_ORG_API_KEY": "your_api_key_here"
      }
    }
  }
}

Environment Variables

RANDOM_ORG_API_KEY (required) - Your api.random.org API key
RATE_LIMIT_REQUESTS_PER_SECOND (optional, default: 1) - Rate limiting
RATE_LIMIT_BURST_SIZE (optional, default: 5) - Burst size for rate limiting
REQUEST_TIMEOUT_MS (optional, default: 10000) - Request timeout in milliseconds
MAX_RETRIES (optional, default: 3) - Maximum number of retries
RETRY_DELAY_MS (optional, default: 1000) - Delay between retries

Usage

🔗 MCP Client Integration

Claude Desktop Configuration

Locate your Claude Desktop configuration file:
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Linux: ~/.config/Claude/claude_desktop_config.json
Add the Random.org MCP server configuration:

{
  "mcpServers": {
    "random-org": {
      "command": "npx",
      "args": ["random-org-mcp-server"],
      "env": {
        "RANDOM_ORG_API_KEY": "your_api_key_here"
      }
    }
  }
}

Restart Claude Desktop

Alternative Configurations

Using Global Installation

{
  "mcpServers": {
    "random-org": {
      "command": "random-org-mcp",
      "env": {
        "RANDOM_ORG_API_KEY": "your_api_key_here"
      }
    }
  }
}

Using Local Installation

{
  "mcpServers": {
    "random-org": {
      "command": "node",
      "args": ["node_modules/random-org-mcp-server/dist/index.js"],
      "env": {
        "RANDOM_ORG_API_KEY": "your_api_key_here"
      }
    }
  }
}

Running the Server

🚀 Production Usage

If installed globally:

random-org-mcp

Using npx (no installation required):

npx random-org-mcp-server

From source:

npm start

🛠️ Development

For development with auto-reload:

npm run dev

Tool Examples

Generate Random Integers

{
  "name": "generateIntegers",
  "arguments": {
    "n": 5,
    "min": 1,
    "max": 100,
    "replacement": true,
    "base": 10
  }
}

Generate Random Strings

{
  "name": "generateStrings",
  "arguments": {
    "n": 3,
    "length": 8,
    "characters": "abcdefghijklmnopqrstuvwxyz0123456789",
    "replacement": true
  }
}

Generate UUIDs

{
  "name": "generateUUIDs",
  "arguments": {
    "n": 5
  }
}

Generate Gaussian Numbers

{
  "name": "generateGaussians",
  "arguments": {
    "n": 10,
    "mean": 0,
    "standardDeviation": 1,
    "significantDigits": 6
  }
}

Get Usage Statistics

{
  "name": "getUsage",
  "arguments": {}
}

API Limits

The api.random.org service has the following limits:

Integers: 1-10,000 numbers per request
Integer Sequences: 1-10,000 sequences, each 1-10,000 numbers long
Decimal Fractions: 1-10,000 numbers per request
Gaussians: 1-10,000 numbers per request
Strings: 1-10,000 strings per request, each 1-20 characters long
UUIDs: 1-1,000 UUIDs per request
Blobs: 1-100 blobs per request, each 1-1,048,576 bytes

Error Handling

The server includes comprehensive error handling:

Input validation for all parameters
Rate limiting to respect API limits
Automatic retries with exponential backoff
Detailed error messages for troubleshooting

Development

Scripts

npm run build - Build the TypeScript code
npm start - Run the compiled server
npm run dev - Run in development mode with auto-reload
npm run clean - Clean the build directory

Project Structure

src/
├── index.ts           # Main entry point
├── server.ts          # MCP server implementation
├── randomOrgClient.ts # API client for random.org
├── rateLimiter.ts     # Rate limiting implementation
├── config.ts          # Configuration management
└── types.ts           # TypeScript type definitions

License

MIT License - see LICENSE file for details.

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

Support

For issues related to this MCP server, please open an issue on GitHub. For api.random.org API issues, please refer to their documentation.

Available Tools

8 tools

generateBlobsB

Generate true random binary data

ParametersJSON Schema

Name	Required	Description	Default
`n`	Yes	Number of blobs to generate (1-100)
`size`	Yes	Size of each blob in bytes (1-1,048,576)
`format`	No	Output format	base64

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Generate true random binary data,' implying a creation operation, but doesn't disclose behavioral traits like whether this requires authentication, has rate limits, or what the output looks like (e.g., format details beyond schema). This is a significant gap for a tool with no annotation coverage.

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 appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.

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 states what the tool does but lacks details on output format, behavioral context, or usage guidelines, making it incomplete for optimal agent 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?

Schema description coverage is 100%, so the schema already documents all parameters (n, size, format) with descriptions, constraints, and defaults. The description adds no additional meaning beyond what the schema provides, such as explaining what 'blobs' are or usage examples, meeting the baseline for high schema coverage.

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 'Generate true random binary data' clearly states the verb ('Generate') and resource ('true random binary data'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like generateStrings or generateUUIDs, which also generate random data but of different types, so it lacks sibling distinction.

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. It doesn't mention sibling tools or contexts where binary data generation is preferred over other random data types, leaving the agent without usage direction.

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

generateDecimalFractionsB

Generate true random decimal fractions between 0 and 1

ParametersJSON Schema

Name	Required	Description
`n`	Yes	Number of decimal fractions to generate (1-10,000)
`decimalPlaces`	Yes	Number of decimal places (1-20)
`replacement`	No	Allow replacement (duplicates)

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions 'true random' (implying quality) and the range (0-1), but lacks critical behavioral details: whether this is a read-only operation, if it has rate limits, how randomness is sourced, or what the output format looks like. For a tool with no annotations, this is a significant gap.

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: 'Generate true random decimal fractions between 0 and 1'. It is front-loaded with the core purpose, has zero wasted words, and appropriately sized for a simple tool. Every part of the sentence adds value.

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 basic purpose but lacks behavioral context, usage guidance, and output details. The schema handles parameters well, but without annotations or output schema, the description should do more to compensate for these gaps.

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 (n, decimalPlaces, replacement) with descriptions and constraints. The description adds no parameter-specific information beyond what the schema provides, such as explaining the interaction between parameters or default behaviors. Baseline 3 is appropriate 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 tool's purpose: 'Generate true random decimal fractions between 0 and 1'. It specifies the verb ('generate'), resource ('true random decimal fractions'), and scope ('between 0 and 1'). However, it doesn't explicitly differentiate from sibling tools like 'generateGaussians' or 'generateIntegers', which likely generate different types of random numbers.

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. It doesn't mention sibling tools like 'generateGaussians' (for normal distributions) or 'generateIntegers' (for whole numbers), nor does it specify use cases or prerequisites. The agent must infer usage from the description alone.

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

generateGaussiansC

Generate true random numbers from a Gaussian distribution

ParametersJSON Schema

Name	Required	Description
`n`	Yes	Number of Gaussian numbers to generate (1-10,000)
`mean`	Yes	Mean of the distribution
`standardDeviation`	Yes	Standard deviation of the distribution
`significantDigits`	Yes	Number of significant digits (2-20)

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates 'true random numbers' but doesn't clarify aspects like whether this is a read-only operation, if it has side effects, rate limits, or authentication requirements. For a tool with no annotation coverage, this is a significant gap in transparency.

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 unnecessary words or fluff. It's appropriately sized and front-loaded, 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 tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a list of numbers), behavioral traits, or usage context relative to siblings. For a 4-parameter tool with no structured support, the description should provide more comprehensive guidance.

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 schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., 'n' as number of Gaussian numbers with range 1-10,000). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.

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 action ('generate') and resource ('true random numbers from a Gaussian distribution'), which is specific and unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generateDecimalFractions' or 'generateIntegers', which would require mentioning the specific distribution type as a distinguishing feature.

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 'generateDecimalFractions' or 'generateIntegers'. There's no mention of 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.

generateIntegersC

Generate true random integers within a specified range

ParametersJSON Schema

Name	Required	Description
`n`	Yes	Number of integers to generate (1-10,000)
`min`	Yes	Minimum value (inclusive)
`max`	Yes	Maximum value (inclusive)
`replacement`	No	Allow replacement (duplicates)
`base`	No	Number base (2, 8, 10, or 16)

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 full burden for behavioral disclosure. While 'generate true random integers' implies a read-only operation, it doesn't address important behavioral aspects: whether there are rate limits, authentication requirements, what 'true random' means (source of randomness), whether results are reproducible, or what format the output takes. The description is too minimal for a tool with 5 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 that states the core functionality without unnecessary words. It's appropriately sized for a straightforward generation tool and front-loads the essential information. Every word earns its place in conveying the tool's purpose.

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 tool's complexity (5 parameters, no output schema, no annotations), the description is insufficiently complete. It doesn't explain the output format, doesn't address the 'true random' claim's implications, and provides no behavioral context. For a generation tool with multiple configuration options and no structured output documentation, the description should do more to help the agent understand what to expect.

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 mentions 'within a specified range' which hints at the min/max parameters, but doesn't add meaningful semantics beyond what the 100% schema coverage already provides. The schema descriptions thoroughly document each parameter's purpose, constraints, defaults, and allowed values. The description provides no additional parameter context, earning the baseline score for high schema coverage.

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 tool's purpose as 'Generate true random integers within a specified range' - a specific verb ('generate') and resource ('true random integers') with scope ('within a specified range'). However, it doesn't differentiate from sibling tools like 'generateIntegerSequences' which might generate sequential rather than random integers, or explain what makes these integers 'true random' versus pseudo-random.

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. There's no mention of alternatives like 'generateIntegerSequences' for sequential generation or 'generateDecimalFractions' for non-integer values. The agent must infer usage from tool names alone, which is insufficient for optimal selection.

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

generateIntegerSequencesC

Generate sequences of true random integers

ParametersJSON Schema

Name	Required	Description
`n`	Yes	Number of sequences to generate (1-10,000)
`length`	Yes	Length of each sequence (1-10,000)
`min`	Yes	Minimum value (inclusive)
`max`	Yes	Maximum value (inclusive)
`replacement`	No	Allow replacement within each sequence
`base`	No	Number base (2, 8, 10, or 16)

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'generate' implies creation, it doesn't specify whether this is a read-only operation, what permissions might be needed, rate limits, or what format the output takes. The 'true random' qualifier adds some context about randomness quality, but overall behavioral traits are minimally described.

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 extremely concise - a single sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential function without unnecessary elaboration.

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?

For a tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the output looks like (e.g., format, structure), doesn't clarify the relationship with sibling tools, and provides minimal behavioral context despite the complexity of generating random sequences.

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 schema description coverage is 100%, providing comprehensive parameter documentation. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't enhance understanding of parameter meanings or relationships.

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

Purpose3/5

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

The description 'Generate sequences of true random integers' clearly states the verb ('generate') and resource ('sequences of true random integers'), but it doesn't distinguish this tool from its sibling 'generateIntegers' - both appear to generate random integers, making the purpose somewhat vague in context.

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?

No guidance is provided about when to use this tool versus alternatives like 'generateIntegers' or other sibling tools. The description offers no context about appropriate use cases, prerequisites, or exclusions.

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

generateStringsC

Generate true random strings

ParametersJSON Schema

Name	Required	Description
`n`	Yes	Number of strings to generate (1-10,000)
`length`	Yes	Length of each string (1-20)
`characters`	Yes	Characters to use for generation
`replacement`	No	Allow replacement within each string

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 'true random strings' but doesn't explain what 'true random' entails (e.g., cryptographically secure, source of randomness), potential rate limits, error conditions, or output format. This is inadequate for a tool with parameters and no output schema.

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, making it easy to parse quickly. Every word earns its place by directly conveying the tool's function.

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 tool has four parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like randomness quality, output format (e.g., array of strings), or error handling, leaving significant gaps for the agent to operate 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, clearly documenting all four parameters (n, length, characters, replacement) with constraints and defaults. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.

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 'Generate true random strings' clearly states the verb ('Generate') and resource ('true random strings'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like generateUUIDs or generateBlobs, which also generate random data but of different 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 generateUUIDs for UUIDs or generateBlobs for binary data. 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.

generateUUIDsB

Generate true random UUIDs (version 4)

ParametersJSON Schema

Name	Required	Description	Default
`n`	Yes	Number of UUIDs to generate (1-1,000)

TDQS

B3.2/5.0

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 states the tool generates UUIDs but doesn't explain key behaviors such as randomness quality, performance characteristics, or error handling. This leaves significant gaps for a tool with potential complexity in random generation.

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, clearly front-loading the core functionality. It's appropriately sized for a simple tool with one parameter.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., format of returned UUIDs, array structure), nor does it cover behavioral aspects like rate limits or error cases, which are important for a generation tool.

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% coverage, fully describing the single parameter 'n' with its type, description, and constraints. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.

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 verb ('Generate') and resource ('true random UUIDs (version 4)'), distinguishing it from sibling tools like generateBlobs or generateStrings by specifying the exact type of data produced. It's precise and unambiguous about what the tool does.

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 generateStrings or generateIntegers, nor does it mention any prerequisites or exclusions. It lacks context for tool selection among siblings.

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

getUsageB

Get API usage statistics

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get API usage statistics' implies a read-only operation, but it doesn't specify details like authentication requirements, rate limits, data freshness, or response format. This leaves significant gaps in understanding the tool's 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 'Get API usage statistics' is a single, clear sentence with no wasted words. It's front-loaded and efficiently communicates the core purpose without unnecessary elaboration.

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 insufficient for a tool that likely returns structured usage data. It doesn't explain what statistics are included, time ranges, or format, leaving the agent with incomplete context for effective use.

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

Parameters4/5

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

The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, earning a high baseline score for this dimension.

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 'Get API usage statistics' clearly states the verb ('Get') and resource ('API usage statistics'), making the tool's purpose immediately understandable. However, it doesn't differentiate this tool from its siblings, which are all data generation tools, so it doesn't reach the highest score.

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. It doesn't mention context, prerequisites, or relationships to sibling tools, leaving the agent with minimal usage direction.

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

Install Server

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TDQS

A3.5/5.0

Disambiguation5/5

Every tool has a clearly distinct purpose focused on generating different types of random data or checking usage. The descriptions specify unique data types (blobs, decimal fractions, Gaussians, integers, sequences, strings, UUIDs) with no overlap in functionality. An agent can easily distinguish between them based on the specific random generation needed.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with 'generate' or 'get' as the verb and a descriptive noun (e.g., generateIntegers, generateUUIDs, getUsage). The naming is uniform across all tools, using camelCase consistently without any deviations or mixed conventions.

Tool Count5/5

With 8 tools, this server is well-scoped for generating various types of random data and checking usage. Each tool serves a specific and necessary function in the domain of random number generation, covering common use cases without being excessive or lacking. The count is appropriate for the server's purpose.

Completeness5/5

The tool set provides complete coverage for the domain of random data generation, including integers, fractions, Gaussians, strings, UUIDs, blobs, sequences, and usage statistics. There are no obvious gaps; it supports a wide range of random generation needs and includes a utility tool for monitoring API usage, ensuring no dead ends for agents.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

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