list_new_releases

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, but the description compensates fully by disclosing cost (~1 credit/call, ~5s latency) and the complex return structure (recsList as JSON string needing secondary parse). It also explains the data nesting (data.json[0].data), ensuring the agent understands the behavior.

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 well-structured with sections (use, don't use, returns, pair). Every sentence serves a purpose; despite its length, it is concise for the complexity covered. Front-loads the core purpose in Chinese then English.

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 absence of output schema, the description thoroughly explains the return format (recsList as parseable JSON) and metadata structure. It also provides cost and pairing guidance, making this a self-contained, complete definition for the tool.

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 100% schema coverage, the description adds significant context: explains how to obtain categorySlug from URL, provides examples for zipcode per site, and clarifies default formats. This goes beyond the schema, enriching understanding.

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 lists New Releases (items selling best within 30 days) for a category, using specific verbs like '拉' and '列出'. It distinguishes from siblings like list_bestsellers (for evergreen bestsellers) and list_category_products (for all products), making the purpose unmistakable.

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 provides when-to-use scenarios (e.g., '类目新品', '黑马新品') and when-not-to-use (e.g., evergreen bestsellers, all products). Also suggests pairing with get_amazon_product for deeper analysis, giving clear guidance on tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.