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The-40-Thieves

codecalc

by The-40-Thieves
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Data Sizes

data_sizes
Read-onlyIdempotent

Convert an integer byte count into binary (KiB/MiB) and decimal (KB/MB) units, exposing the discrepancy between them.

Instructions

Byte counts for a plain integer, both binary and decimal — the gap between them is where '291 MB' and '277 MiB' silently disagree by 5%. For units other than bytes, use convert_units. For a duration, not a byte count, use human_duration. Returns bytes plus binary and decimal dicts of unit -> value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nYesByte count to express in both binary (KiB/MiB/GiB/TiB, /1024) and decimal (KB/MB/GB/TB, /1000) units

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.0
    • addedInput schema / properties / n / description
      Added value: +"Byte count to express in both binary (KiB/MiB/GiB/TiB, /1024) and decimal (KB/MB/GB/TB, /1000) units"
  2. Changed1 schema field changedv0.11.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "data_sizesDictOutput",
+  "type": "object"
+}
  3. Changed4 schema fields changedv0.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / n / title
      Added value: +"N"
    • addedInput schema / title
      Added value: +"data_sizesArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  4. First observedv0.1.0

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds the return structure ('bytes' plus 'binary' and 'decimal' dicts) and clarifies the meaning of the two unit systems, beyond what annotations provide. It doesn't mention edge cases like negative integers, but that's more parameter semantics.

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 concise, with no fluff. It front-loads the core purpose, then provides usage routing, and ends with the return format. Every sentence adds value and the structure is easy to parse.

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, one-parameter, read-only tool with full schema coverage and an output schema, the description covers everything needed: purpose, usage boundaries, return format, and unit definitions. Nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter n has a description covering its semantics completely: it specifies the exact units used (KiB/MiB/GiB/TiB vs KB/MB/GB/TB) and the divisors (/1024 vs /1000). Schema coverage is 100%, and the description reinforces the parameter's purpose.

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 clearly states the tool returns byte counts for a plain integer in both binary and decimal units, and explicitly names the sibling tools it is not (convert_units for other units, human_duration for durations). This distinguishes it from all alternatives without needing to inspect schemas.

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

Usage Guidelines5/5

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

It gives explicit when-to-use guidance: for byte counts only, and directly routes to alternatives for other cases. The example of the 5% discrepancy adds context for when the distinction matters, leaving no ambiguity about appropriate invocation.

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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