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sf-beverage-skus

price_order

Compute an order total: total_cents = sku.price_cents + SUM(option.price_delta_cents). Validates option_ids against the SKU; rejects unknown or inapplicable options.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sku_idYes
option_idsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: the computation model and that it validates option_ids against the SKU, rejecting unknown or inapplicable options. It does not explicitly say the operation is a pure calculation with no persistence or side effects, which is the remaining gap.

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 tight sentences with zero filler; the formula, the most decision-relevant content, is front-loaded before the validation rule.

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, and the description compensates by expressing the result shape (total_cents) via the formula. For a two-parameter pure-calculation tool this is nearly complete, lacking only explicit confirmation of no side effects and error semantics.

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 description coverage is 0%, so the description must compensate. It names option_ids and explains they are validated against the SKU, adding real meaning, but sku_id is never described and the array-of-ids nature of option_ids is left to the 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 precise verb (Compute) plus the resource (an order total) and even gives the exact formula total_cents = sku.price_cents + SUM(option.price_delta_cents). It is unambiguously distinguishable from the read-only siblings get_sku and search_skus.

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

Usage Guidelines3/5

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

Usage is implied by the pricing formula and validation clause, but the description never states when to call this versus the sibling SKU tools or any prerequisite (e.g., must the SKU already exist?). Implicit context only.

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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