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okoshi-f

Random Value MCP Server

by okoshi-f

generate_random_string

Generate random alphanumeric strings of specified length for testing, security tokens, or unique identifiers.

Instructions

Generate a random string of specified length using alphanumeric characters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lengthYesLength of the string to generate

Implementation Reference

  • The handler implementation for generate_random_string, which validates the input length and generates a random alphanumeric string.
    case "generate_random_string": {
      const { length } = args as { length: number };
      
      if (length < 1) {
        throw new Error("String length must be at least 1");
      }
      
      const characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
      let result = "";
      
      for (let i = 0; i < length; i++) {
        result += characters.charAt(Math.floor(Math.random() * characters.length));
      }
      
      return {
        content: [
          {
            type: "text",
            text: `Generated random string: ${result}`,
          },
        ],
      };
    }
  • The registration and schema definition for the generate_random_string tool.
    {
      name: "generate_random_string",
      description: "Generate a random string of specified length using alphanumeric characters",
      inputSchema: {
        type: "object",
        properties: {
          length: {
            type: "number",
            description: "Length of the string to generate",
            minimum: 1,
          },
        },
        required: ["length"],
      },
    },
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 the output type ('random string') and character set ('alphanumeric characters'), but fails to cover important traits like whether the generation is deterministic, any rate limits, error handling, or the exact format of the output. This leaves significant gaps for a tool that produces 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 that directly states the tool's function without any unnecessary words. It is front-loaded and wastes no space, 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 covers the basic purpose but misses critical details like the output format (e.g., string type, encoding), behavioral aspects (e.g., randomness source, constraints), and usage context. For a tool with no structured support, this leaves the agent under-informed.

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 the 'length' parameter well-documented in the schema itself. The description adds minimal value by implying the parameter's role ('specified length') but doesn't provide additional semantics beyond what the schema already states, 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 clearly states the action ('Generate a random string') and specifies the resource ('of specified length using alphanumeric characters'), making the purpose understandable. However, it doesn't explicitly differentiate from the sibling tool 'generate_random_number', which is a minor gap.

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 the sibling 'generate_random_number' or other string generation methods. It lacks context on prerequisites, use cases, or exclusions, leaving the agent with minimal direction.

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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