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okoshi-f

Random Value MCP Server

by okoshi-f

generate_random_number

Generate a random integer within a specified range using minimum and maximum values. This tool creates random numbers for applications requiring randomization.

Instructions

Generate a random integer within a specified range

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
minYesMinimum value (inclusive)
maxYesMaximum value (inclusive)

Implementation Reference

  • The handler implementation for 'generate_random_number' inside the CallToolRequestSchema switch case.
    case "generate_random_number": {
      const { min, max } = args as { min: number; max: number };
      
      if (min > max) {
        throw new Error("Minimum value cannot be greater than maximum value");
      }
      
      const randomNumber = Math.floor(Math.random() * (max - min + 1)) + min;
      
      return {
        content: [
          {
            type: "text",
            text: `Generated random number: ${randomNumber}`,
          },
        ],
      };
    }
  • The tool registration and schema definition for 'generate_random_number' within the ListToolsRequestSchema handler.
    {
      name: "generate_random_number",
      description: "Generate a random integer within a specified range",
      inputSchema: {
        type: "object",
        properties: {
          min: {
            type: "number",
            description: "Minimum value (inclusive)",
          },
          max: {
            type: "number", 
            description: "Maximum value (inclusive)",
          },
        },
        required: ["min", "max"],
      },
    },
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 mentions generating a random integer but fails to add context beyond the basic action, such as randomness quality, performance, or any limitations (e.g., distribution, seed, rate limits). This leaves gaps in understanding the tool's behavior for an AI 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 directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized, making it easy to understand quickly 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 low complexity (two simple parameters, no output schema, no annotations), the description is minimally adequate but lacks depth. It covers the basic purpose but misses behavioral context and usage guidelines, making it incomplete for optimal agent use, though not severely deficient for such a simple 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% description coverage, with clear parameter descriptions for 'min' and 'max'. The description adds no additional meaning beyond the schema, such as constraints or examples, but since the schema is comprehensive, the baseline score of 3 is appropriate as it doesn't compensate for any gaps.

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 generating a random integer within a specified range, using a specific verb ('generate') and resource ('random integer'), which is straightforward. However, it does not explicitly differentiate from its sibling tool 'generate_random_string', which likely generates strings instead of integers, so it misses full 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, such as the sibling 'generate_random_string' or other random generation methods. It lacks context on use cases, exclusions, or prerequisites, offering only a basic functional statement without usage instructions.

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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