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mcp-packing-list

Pack goods into a carton

pack_item

Add a quantity of an item to a named carton on a packing list, specifying description, count, and optional per-unit weight. Split items across cartons with separate calls.

Instructions

Put a quantity of one item into a named carton: a description, how many, and the per-unit weight in WHOLE GRAMS. Leave unit_grams out when it was not weighed and the carton gross comes back as a lower bound.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skuNoStock code, upper-cased and stripped of spaces. Lines with a SKU are matched on it, lines without on their description
noteNo
cartonYesThe carton id, e.g. C01, or its exact label
quantityYesHow many units go into THIS carton. Split across cartons with one call each
unit_gramsNoWeight of ONE unit in whole grams. Omit when not weighed; the carton is then a lower bound
descriptionYesThe goods going in, e.g. Oak shelf 900mm
packing_listYesThe packing list id, e.g. PL-2026-0001, or the order reference when only one list carries it

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.21.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses one meaningful behavior—omitting unit_grams makes the returned carton gross a lower bound—but it does not explain the mutation's scope (e.g., whether items are added to an existing carton, whether the packing list must exist, or any reversibility), so it is only partially transparent.

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 sentences with no filler. The core action is front-loaded and the conditional unit_grams guidance is kept in a separate, clearly readable second sentence.

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

Completeness2/5

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

For a 7-parameter mutation tool with no annotations and no output schema, the description is too thin. It omits when to use it versus alternatives, side effects on the carton/packing list, prerequisites (e.g., carton existence), and an overall return value description beyond the lower-bound hint.

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 high (86%), so the baseline is 3. The description reinforces the whole-grams constraint and the omit-when-not-weighed rule, but those points are already in the schema; it adds little beyond it for sku, packing_list, note, or carton.

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 states a specific action—packing a quantity of one item into a named carton—and names the main fields involved. It is clear, but it does not explicitly contrast it with siblings such as unpack_item or packing_expect, so it earns 4 rather than 5.

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?

There is no guidance on when to call pack_item versus the other packing-list tools, and no exclusions or prerequisites are mentioned. The only conditional sentence is about omitting unit_grams, which is parameter behavior rather than tool-selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.