pack_calculatorExploration tool for item dimensions and weight. Calculates required inside dimensions, ranks Packrift box/mailer candidates, and gives void-fill guidance before live price/inventory confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| use_case | No | auto | |
| padding_in | No | ||
| item_depth_in | Yes | ||
| item_width_in | Yes | ||
| item_length_in | Yes | ||
| item_weight_lb | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds context that this is an exploration tool, not for final confirmation. It does not contradict annotations and provides useful extra context about the tool's role in the workflow.
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?
The description is a single sentence that front-loads the tool's purpose and includes all key behavioral points without unnecessary words. It is concise and well-structured.
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 description provides a good high-level context but lacks detail on output format and specific parameter semantics. Given the tool has 7 parameters and no output schema, the description is somewhat incomplete for full decision-making, though it is adequate for basic selection.
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?
With zero schema description coverage, the description must compensate but only mentions item dimensions and weight. It does not explain the roles of limit, use_case, or padding_in, leaving some parameters under-specified, though the core inputs are covered.
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 the tool calculates required inside dimensions, ranks Packrift box/mailer candidates, and provides void-fill guidance. It distinguishes itself from siblings by positioning as an exploratory pre-confirmation tool, with a specific verb+resource structure.
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?
The description clearly indicates when to use this tool ('before live price/inventory confirmation') and its exploratory nature, but it does not explicitly mention alternatives or when not to use it, so it falls short of full 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.