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SoapyRED

FreightUtils MCP Server

by SoapyRED

pallet_fitting_calculator

Calculate optimal box arrangement on pallets for shipping and warehouse planning. Determines maximum boxes per pallet considering stacking layers, 90-degree rotation, weight limits, and volume utilization.

Instructions

Calculate how many boxes fit on a pallet (layers, rotation, weight limits).

This tool determines the optimal arrangement of identical boxes on a pallet, accounting for:

  • Layer-by-layer stacking up to the max height

  • 90-degree rotation to find the best fit

  • Weight capacity limits

  • Volume utilisation percentage

Use this tool when you need to:

  • Plan pallet loading for warehouse/shipping

  • Calculate total boxes per pallet

  • Check if weight limits will be reached before space runs out

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pallet_length_cmYesPallet length in cm
pallet_width_cmYesPallet width in cm
pallet_max_height_cmYesMaximum stack height in cm (including pallet deck)
pallet_deck_height_cmNoPallet deck height in cm (default: 15)
box_length_cmYesBox length in cm
box_width_cmYesBox width in cm
box_height_cmYesBox height in cm
box_weight_kgNoBox weight in kg
max_payload_kgNoMaximum pallet payload weight in kg
allow_rotationNoAllow 90-degree box rotation (default: true)
Behavior4/5

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

No annotations provided, but description discloses algorithmic behavior (90-degree rotation optimization, layer-by-layer stacking, weight-before-space checking) and output metrics (volume utilization percentage).

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?

Excellent front-loading with summary parenthetical, followed by mechanism bullets and usage bullets; every sentence earns its place with no redundancy.

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?

No output schema exists, but description adequately covers implied outputs (box count, layers, utilization percentage); slightly more detail on return structure would achieve completeness given parameter complexity.

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 has 100% description coverage; description adds minimal semantic value beyond schema but appropriately references key parameters (rotation, deck height inclusion) to explain logic.

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?

Specific verb ('Calculate') + resource ('boxes fit on a pallet') with distinguishing features (layers, rotation) that clearly differentiate from volume-based siblings like cbm_calculator.

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?

Explicit 'Use this tool when' section with three concrete scenarios, but lacks explicit 'when not to use' guidance or comparison to alternatives like cbm_calculator.

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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