get_productUse after find_packaging_for_item or search_products to pull full detail for a handle: all variants, SKUs, dimensions, weight, stock. Input: handle. Call before building a cart to map qty to the right variant.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful return-content context (all variants, SKUs, dimensions, weight, stock). It does not mention error cases or rate limits, but for a read-only detail retrieval the provided transparency is sufficient.
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?
Three concise, front-loaded sentences convey purpose, workflow position, and input without wasted words. Every sentence adds operational value.
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?
Given the simple single-parameter schema and no output schema, the description adequately covers intended usage and return contents. It could be more complete by defining 'handle,' but overall it provides sufficient context for an agent to invoke the tool effectively.
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?
The schema has 0% description coverage, and the description only says 'Input: handle.' It implies handle is an identifier but does not explain what constitutes a handle (e.g., slug, SKU, internal ID) or provide format examples. This leaves the agent to infer the parameter meaning.
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 pulls full product detail for a handle, listing variants, SKUs, dimensions, weight, and stock. This distinguishes it from sibling tools like search_products and find_packaging_for_item, which are preliminary lookup steps.
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?
Explicitly places the tool in a workflow: 'Use after find_packaging_for_item or search_products' and 'Call before building a cart to map qty to the right variant.' This provides clear when-to-use context and sequencing relative to alternatives.
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.