measure-mcp

fba_size_tier

Classify a packaged unit into its US Amazon FBA size tier from packed dimensions and weight, then show its margin to each tier limit.

Instructions

Classify a packaged unit into its US Amazon FBA size tier (small standard, large standard, small bulky, large bulky, extra-large bands) and show how close it is to each limit. (fba_analyze answers the usual question, tier plus fee plus nearest limit, in one call.)

Inputs: dims = the 3 edges of the PACKED unit, any order: "12 x 9 x 3 in", [12, 9, 3] with dims_unit (in default, cm or mm) or {"length":12,"width":9,"height":3}; weight = unit weight of the packed unit: 1.4, "1.4 lb", "22 oz" or "640 g" (a bare number is POUNDS unless weight_unit is oz, lb, g or kg; the summary states how it was read). dims_sigma = 1-sigma measurement error per edge in dims_unit (a single number, or 3 numbers in the same order as dims; omit for exact tape/caliper values); weight_sigma_oz = scale error in ounces. as_of (ISO date) picks the dataset effective on that date, default today. Only product_class "standard" is modelled in v0.1.

Returns tier, sorted dims in inches, length_plus_girth_in, unit and dimensional weight in lb (divisor 139, 2 in floor on the two shorter sides for bulky tiers), the shipping weight Amazon bands on (greater of unit and dimensional, unit-only for small standard), each binding limit with margin, z-score and risk class (safe/risky/critical), rejected tiers with the failing limits, the next tier up and tier_probabilities when dims_sigma is given. detail="summary" (default) adds a short caveats list; detail="full" adds the assumptions list and disclaimer.

Estimate from third-party-compiled rules (see fba_data_status); Amazon's own measurement governs.

Input Schema

TableJSON Schema

Name	Required	Default
`dims`	Yes
`as_of`	No
`detail`	No	summary
`weight`	Yes
`dims_unit`	No
`dims_sigma`	No
`weight_unit`	No
`product_class`	No	standard
`weight_sigma_oz`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 16, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral load and does so well: it discloses the dimensional-weight divisor (139), the 2-inch floor, shipping-weight banding rules, risk classes, and that the result is an estimate from third-party rules where 'Amazon's own measurement governs.' Some of the 'Returns...' enumeration overlaps with the output schema rather than adding behavior, keeping it at 4.

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 dense but front-loads the purpose and separates 'Inputs:' and 'Returns:' cleanly. The long Returns sentence partially restates the available output schema, which is the main source of avoidable length given a 9-parameter tool that genuinely needs input detail.

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 9-parameter, 0%-coverage tool with an output schema, the definition covers all inputs with format rules, states the estimate/disclaimer caveat, and explains detail-dependent return content. Nothing an agent needs to call it correctly appears to be 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?

Schema description coverage is 0% across 9 parameters, so the description must compensate and it does: dims formats, dims_unit default/cm/mm, weight formats and the pounds-unless-unit-given rule, weight_sigma_oz, as_of dataset selection, detail modes, and that only product_class "standard" is modelled. Every parameter is given meaning beyond the bare schema.

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?

States a specific verb and resource ('Classify a packaged unit into its US Amazon FBA size tier') and names the tier bands. It also names the sibling fba_analyze and clarifies the division of labor, so an agent can distinguish it from the other FBA tools without opening a schema.

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 parenthetical pointing to fba_analyze for 'the usual question, tier plus fee plus nearest limit' implies when to prefer this tool (tier-only). It also gives a clear usage condition for dims_sigma ('omit for exact tape/caliper values'). No explicit when-not statement or prerequisites, so it falls just short of 5.

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