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Faire: Update inventory

faire_update_inventory

Faire: Set the available quantity of product variants by SKU. Sets the available quantity for one or more variants by SKU, so Faire matches your warehouse. It reaches no one directly and one more call restores the old numbers, so update_inventory runs without asking first. Needs the user's Cotlet API key with access to this app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesList of { sku, quantity }.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare destructiveHint=false and openWorldHint=true, yet the description adds real value beyond them: it discloses that the call notifies no one, that the change is reversible ('one more call restores the old numbers'), and that it requires a Cotlet API key with app access. These are exactly the auth and reversibility facts annotations do not carry.

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?

Purpose is front-loaded in the first sentence and the tool is compact. However, the second sentence largely restates the first ('Sets the available quantity for one or more variants by SKU'), so there is mild redundancy that a tighter phrasing could remove.

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?

With one simple parameter, no output schema and annotations covering safety, the description supplies the remaining essentials: what it changes, that it is silent and reversible, and the auth requirement. A note on the batch size limit or partial-failure behavior would round it out, but nothing critical is missing.

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 100% and the single 'items' parameter is already documented as a list of { sku, quantity }. The description reinforces the SKU-keying and bulk nature ('one or more variants by SKU'), but adds no format or syntax detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource ('Set the available quantity of product variants by SKU') and names the SKU-keyed scope, which is enough to distinguish it from the read-side faire_get_inventory. It stops short of naming that sibling explicitly, so it is clear but not fully self-differentiating.

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?

The description implies usage conditions ('It reaches no one directly and one more call restores the old numbers, so update_inventory runs without asking first') and gives a prerequisite (Cotlet API key with app access), but never states when to prefer this over faire_get_inventory or when not to call it. Guidance is present but inferred rather than explicit.

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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