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The Quartermaster

Plan an outfit

quartermaster_plan_outfit
Read-onlyIdempotent

Prescribe a complete outfit for an occasion within a budget. Every piece gets the best-graded maker that fits the money at a real price (full price or Nordstrom Rack), a second choice, and what to check at the fitting, plus cloth, cut and tag advice. Makers come only from the Quartermaster ledger, checked September 2026. Use after the fitting questions, or straight away when someone gives the occasion and the money ("wedding in October, $800, what do I get?").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cutNoJacket or suit cut, if tailoring is involved.
roomYesThe occasion.
clothNoJacket or suit cloth, if tailoring is involved.
budgetYesTotal to spend on the outfit, in US dollars.
impressionNoHow they want to come across: quiet (quiet and correct, the default), sharper (a notch above the room), relaxed (at ease).
already_ownNoPieces they already own for this occasion; the plan builds around them.
time_of_dayNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With readOnlyHint/idempotent/destructive already declared by annotations, the description adds real value: makers come only from the Quartermaster ledger, freshness is stated ('checked September 2026'), and pricing behavior is disclosed ('real price (full price or Nordstrom Rack)'). The response structure is also previewed. It does not discuss limits on catalog coverage or what happens when no maker fits the budget.

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 core action is front-loaded in the first sentence, followed by output details and usage triggers. It is dense but every sentence carries information; the single long paragraph is slightly heavy but not padded.

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 read-only planning tool with no output schema and 7 well-documented parameters, the description covers purpose, inputs' effect, data provenance, pricing model, and when to call it. An agent has everything needed to select and invoke it correctly.

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

Parameters4/5

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, but the description adds meaning about how budget is consumed ('best-graded maker that fits the money at a real price') and how cloth/cut feed the tailoring advice. It enriches the schema rather than merely repeating it.

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 ('prescribe') and resource ('a complete outfit for an occasion within a budget'), and unpacks what the output contains (best-graded maker, second choice, fitting checks, cloth/cut/tag advice). This clearly separates it from siblings like get_maker and check_piece, which handle single entities rather than a full plan.

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?

Gives explicit context: 'Use after the fitting questions, or straight away when someone gives the occasion and the money,' with a worked example ('wedding in October, $800, what do I get?'). It implies the relationship to start_fitting but stops short of naming when to prefer the sibling tools instead.

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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