List YOUR saved items (wishlist) on this merchant, newest first. Scoped to your own agent identity, so you never see another agent's list. REQUIRES IDENTITY: Facet KYA as Authorization: Bearer <kya>.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max items to return. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return ordering ('newest first'), isolation ('never see another agent's list'), and an authentication requirement ('REQUIRES IDENTITY: Facet KYA...'). This goes beyond a minimal description, though it omits details like potential errors or defaults.
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 two sentences long, with the first sentence efficiently packing verb, resource, scope, and ordering. The second sentence delivers a critical auth requirement without excess. Every word earns its place.
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?
For a simple list tool with one optional parameter and no output schema, the description provides the essential context: what items are listed, scope, ordering, and authentication. Given the low complexity, this is fully sufficient for an agent to select and invoke the tool correctly.
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 input schema covers 100% of the parameter documentation ('limit' → 'Max items to return'). The tool description adds no additional parameter semantics beyond what the schema already provides, so a baseline score of 3 is appropriate.
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 uses a specific verb ('List') and clearly identifies the resource ('YOUR saved items (wishlist) on this merchant') and ordering ('newest first'). It also distinguishes from siblings by emphasizing user/agent scoping, which separates it from other list tools like list_orders.
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 states the tool returns the caller's own wishlist items and is scoped to the agent identity, indicating when it should be used. It does not explicitly mention alternatives or exceptions, but the scope is unambiguous enough to guide selection.
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 map to a distinct lifecycle step, and the detailed descriptions separate discovery, cart, checkout, payment, order, and wishlist concerns well. However, get_product and lookup_catalog are largely the same lookup by identifier, discover_products and search_catalog overlap across scope, and update_checkout is actually a read-only no-op.
The set overwhelmingly follows a verb_noun pattern: create_cart, get_checkout, cancel_checkout, complete_checkout, search_catalog. Minor deviations are the wishlist_* resource-first subgroup and the misleading update_checkout, which does not perform an update.
At 23 tools, this sits at the heavy end of the range, though the broad commerce lifecycle does justify substantial surface area. The count is inflated by redundant product lookup tools and a no-op update_checkout, so a more trimmed set in the low-to-mid teens would be healthier.
The server covers the full shopper lifecycle well: discovery, product lookup, cart, checkout, payment, order placement, order read, refunds, and wishlist. Gaps are minor and workable, such as update_checkout not actually supporting updates and no explicit order-cancellation path once a checkout is complete.
