APIVerve

Random Data Generator

RandomDataGenerator

Read-only

Random Data Generator: Random Data Generator produces random numbers in any range (whole or decimal, with or without repeats), dice of any size, coin flips, booleans, random strings, UUIDs, and mock names, emails, addresses and dates for application testing. Numbers, dice, coins, booleans and strings come from a cryptographically secure generator. Paid plans can generate batches up to 100 items. (Cost: 2 credits)

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": "RandomDataGenerator"
  }
}

Input Schema

Name	Required	Description	Default
`max`	No	Largest number to draw, inclusive (type=number). Defaults to 999999
`min`	No	Smallest number to draw, inclusive (type=number). Defaults to 0
`list`	No	The action to perform
`type`	No	The type of random data to generate	number
`count`	No	The number of random data to generate (1 to 100)
`sides`	No	Number of sides on each die for type=dice (2 to 1000)
`length`	No	Length of each value for type=string (1 to 256)
`unique`	No	Draw without repeats (type=number with whole numbers, or type=dice)
`charset`	No	Characters for type=string	alphanumeric
`decimals`	No	Decimal places for type=number (0 to 10); 0 draws whole numbers
`includeAvatar`	No	Include an AI-generated face avatar (only applies when type=name)

Output Schema

Name	Required	Description
`data`	No
`error`	No	Error message, null on success
`status`	No	"ok" on success

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

A4/5.0

Behavior5/5

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

Beyond the annotations, the description discloses that several output types use a cryptographically secure generator, that paid plans support batches up to 100 items, and that invocation costs 2 credits. These are meaningful operational details not captured by the readOnly/openWorld/non-destructive annotations.

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

Conciseness4/5

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

The description is front-loaded with capabilities and then adds security, batch-limit, and cost context in compact sentences. The opening 'Random Data Generator: Random Data Generator' is redundant, but the rest is dense and purposeful.

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 an 11-parameter multi-purpose generator with an output schema and strong annotations, the description covers the main output types, security behavior, limit, and cost. The only notable gap is that the `list` parameter/action is not explained, and sibling routing is not addressed.

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 coverage is 100%, so the baseline is 3, but the description adds useful semantic mapping: whole versus decimal numbers, repeats versus no repeats, dice size, and the range of supported output types. It does not clarify the somewhat vague `list` parameter, keeping it below a 5.

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 resource and enumerates specific output types: numbers, dice, coin flips, booleans, strings, UUIDs, and mock personal data. It does not explicitly differentiate itself from specialized siblings such as DiceRoller, CoinFlipper, or UUIDGenerator, so it falls short of 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 Guidelines3/5

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

Usage is implied through the phrase 'for application testing,' and the broad enumeration of data types suggests when the tool is relevant. However, it gives no explicit guidance on when to prefer this broad generator over specialized siblings, nor any when-not-to-use guidance.

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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