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Glama

random-number

Read-onlyIdempotent

Generate one or more random numbers in [min, max]. By default returns integers; pass integer=false for floats. Uses Math.random() (not crypto-strong).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxYesInclusive upper bound.
minYesInclusive lower bound.
countNoHow many numbers to generate. Defaults to 1. Max 1000 per call.
integerNoIf true, returns integers only. Defaults to true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
maxYesUpper bound used (inclusive).
minYesLower bound used (inclusive).
countYesHow many numbers were generated.
integerYesWhether integers were requested.
numbersYesArray of generated numbers.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Beyond annotations that already mark it read-only, idempotent, and non-destructive, the description adds valuable behavioral details: it uses Math.random(), explicitly warns this is not cryptographically strong, and clarifies default integer behavior. This gives the agent critical information about randomness quality for sensitive use cases.

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 two concise sentences, front-loaded with the core purpose, and every clause adds useful information. No filler or repetition.

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

Completeness5/5

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

For a simple random-number generator with four well-documented parameters and an available output schema, the description covers purpose, safety, randomness quality, and configuration. It is complete enough for an agent to invoke correctly without additional context.

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 coverage is 100% and the description mostly mirrors the schema (inclusive bounds, integer default, count). It adds context by framing parameters in a usage sentence, but does not provide material meaning beyond the schema's own parameter descriptions.

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 opens with a specific action ('Generate one or more random numbers in [min, max]') and clearly distinguishes the tool from siblings by explicitly covering range, count, and integer/float modes. It is unambiguous and self-contained.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

The description implies the tool should be used whenever random numbers in a numeric range are needed, and explains how to switch between integer and float outputs. However, it does not mention alternatives (e.g., uuid-generator for random IDs) or explicitly state when not to use it.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes. However, 'hex-to-rgb' is redundant with 'color-converter', which already handles hex-to-RGB conversion, causing potential confusion.

Naming Consistency4/5

Names follow a consistent lowercase-with-hyphens style, but vary in pattern (e.g., 'angle-converter', 'average-calculator', 'dedup-lines'). One tool ('internal-do-not-call') deviates from the descriptive norm.

Tool Count2/5

With 46 tools, the server is heavily populated. Many converters could be merged into a generic unit converter, and there is redundancy, making the surface unnecessarily large for a single server.

Completeness4/5

The server covers a broad range of utility domains: converters, text processing, math, cryptography, etc. Minor redundancies exist (e.g., hex-to-rgb vs color-converter), but the set is otherwise comprehensive.

Resources