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random-mcp

random-mcp

by greatoldcactus
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random_normal

Generate a single random sample from a normal distribution by specifying mean and standard deviation, with defaults of 0 and 1.

Instructions

Return one normal sample; defaults to mean 0 and standard deviation 1.

Parameters must be finite and standard_deviation must be positive.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "random_normal"
  }
}

Input Schema

NameRequiredDescriptionDefault
meanNo
standard_deviationNo

Output Schema

NameRequiredDescriptionDefault
valueYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It adds non-obvious constraints: parameters must be finite and standard_deviation must be positive. It also states the defaults (0 and 1), covering the main edge cases for a sampling tool.

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?

Two short sentences: the first states behavior and defaults, the second states parameter constraints. There is no filler and no repetition of schema details.

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

Completeness4/5

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

For a simple tool with no required parameters and an output schema, the description supplies the distribution, defaults, and validity constraints needed to call it correctly. It does not explain return values, which is acceptable because an output schema exists. Missing explicit sibling routing is a minor gap given the low complexity.

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?

Schema description coverage is 0%, so the description must compensate. It conveys parameter meanings through the defaults and adds constraints ('finite', 'standard_deviation must be positive') that are not in the schema. The parameter names mean and standard_deviation are self-explanatory, and the added constraints make invocation safe.

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 states the exact operation ('Return one normal sample') and the distribution, distinguishing it from siblings such as roll_dice and random_interval. It also specifies defaults for mean and standard deviation, making the tool's behavior unambiguous.

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 given about when to use this tool versus random_number, random_interval, or roll_dice. The only usage signal is the statistical operation itself, which is implied rather than explicitly stated. An agent must infer appropriateness from the name and description.

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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