Most-wishlisted products (180-day demand signal across all LMK lists, aggregate counts only). Unique LMK demand data.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| maxPrice | No | ||
| minPrice | No |
| Name | Required | Description | Default |
|---|---|---|---|
| products | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context that results are 'aggregate counts only' over a 180-day window, which clarifies the scope and limitations. No contradictions found.
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 very concise at two sentences, with front-loaded key information (most-wishlisted, 180-day, aggregate). However, the second sentence ('Unique LMK demand data') adds limited value and could be merged or expanded.
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 output schema exists, the description adequately conveys what the tool returns. However, parameter semantics are entirely missing, which is critical for correct invocation. The tool has 3 optional parameters with no description, making it incomplete for an agent to use properly.
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 has zero description coverage for parameters (limit, maxPrice, minPrice). The description does not mention any parameters, leaving the agent to infer their purpose from names alone. This is a significant gap.
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 that the tool returns 'most-wishlisted products' based on a 180-day demand signal from LMK lists, with aggregate counts only. The name itself includes 'get' and the description distinguishes it from sibling tools like get_trending_products by emphasizing 'Unique LMK demand data.'
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 implies usage for retrieving wishlist-based demand data but does not provide explicit guidance on when to use this tool versus alternatives such as get_trending_products or get_deals. No when-not-to-use or prerequisite information is given.
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 are distinct, but 'fetch' and 'get_product' have overlapping functionality (both provide full detail and buying options), and 'search' vs 'search_catalog' could confuse agents about which to use for general search. Otherwise clear.
Predominantly uses 'get_' prefix for retrieval tools and 'search' for search tools, but 'fetch' and 'lookup_catalog' deviate slightly from the pattern, reducing consistency.
13 tools is well-scoped for a commerce server covering product discovery, pricing, deals, collections, and gift ideas without being overwhelming or sparse.
Covers key product research and price comparison workflows, but lacks tools for user interaction (e.g., wishlists, purchasing) or multi-product comparisons, which would enhance completeness.
