inventory_statusLive inventory exploration for one or more catalog variants. Returns total quantity, available-for-sale state, warehouse-level quantities where available, and a plain-language fulfillment summary.
| Name | Required | Description | Default |
|---|---|---|---|
| sku | No | Packrift SKU such as 1066. | |
| handle | No | Packrift product handle. | |
| quantity | No | ||
| variant_ids | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and openWorldHint annotations, the description adds useful context: 'live' indicates real-time data, and 'plain-language fulfillment summary' explains the output style. The caveat 'where available' is an honest disclosure of potential data gaps, which is valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with a clear list of return values. No redundant phrasing and each clause adds information. Extremely efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the return description is helpful. However, it omits guidance on how the four optional parameters relate (e.g., sku vs. variant_ids) and what quantity represents in an inventory query. Adequate but with clear gaps around parameter usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 50%; the description does not explain the meaning or usage of quantity or variant_ids. It refers to variants collectively but not how to pass identifiers or how quantity is interpreted. It fails to compensate for the undocumented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is for inventory exploration and lists specific return data (total quantity, available-for-sale state, warehouse-level quantities, fulfillment summary). This differentiates it from a generic inventory check via the 'warehouse-level' and 'plain-language' aspects, though 'exploration' is a somewhat imprecise verb.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied (when you need inventory quantities for variants), but there is no explicit 'when not to use' or mention of alternatives like check_inventory. The phrase 'for one or more catalog variants' gives context but no exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes, but there is potential confusion between inventory-checking tools (check_inventory vs. inventory_status) and cart-handoff tools (create_cart_url vs. prepare_purchase_handoff). Descriptions help differentiate, but an agent might occasionally select the wrong tool.
The naming convention is generally consistent with verb_noun in snake_case (e.g., find_packaging_for_item, get_pricing). However, google_retail_ai_finder breaks the pattern with a company prefix, and some verbs vary (check vs. inventory_status). Overall clear and predictable.
16 tools is slightly above the ideal 3-15 range, but still reasonable for a packaging e-commerce domain covering search, fit, inventory, pricing, shipping, and cart creation. Each tool serves a specific need, so the count feels appropriate.
The tool surface covers the core customer journey: product discovery, fit calculation, inventory/pricing checks, shipping estimation, cart handoff, reordering, and bulk quotes. Minor gaps exist (e.g., no order tracking or cancellation), but these are likely out of scope.