search_amazon_alexa

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

No annotations are provided, so the description carries full burden. It discloses key behavioral traits: cost (6 points per call regardless of prompt count), performance advice (≤3 prompts for stability, average ~30s), return format details (snake_case follow_up_questions, top-level fields), and screenshot parameter behavior (increases backend burden). This exceeds typical transparency.

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 moderately long but well-organized with bullet-like markers, front-loading purpose and usage. Every sentence contributes unique information, though some redundancy exists (e.g., cost mentioned twice). Still, it is efficient for the complexity.

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?

Despite lacking an output schema, the description thoroughly explains the return structure (data.json with fields like prompt, content, products with nested items, follow_up_questions, screenshot, plus top-level taskId/url/screenshot). It covers cost, performance, and usage caveats, making the tool fully self-explanatory for an AI agent.

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 coverage is 100% with both parameters fully described. The description adds significant value: for 'prompts', it explains cost independence, suggests ≤3 prompts for stability, and provides examples; for 'screenshot', it clarifies when to enable (for user-facing evidence) and the trade-off. This goes well beyond the schema.

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 queries Amazon's AI shopping assistant Rufus with natural language prompts to get structured product recommendations, text replies, and follow-up suggestions. It explicitly distinguishes from sibling tools like search_amazon (keyword search), list_bestsellers (category bestsellers), and get_amazon_product (single ASIN details), making the purpose unambiguous.

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 provides explicit when-to-use scenarios (e.g., '用户说问Amazon AI X', 'Rufus推荐', '按场景找产品') and when-not-to-use alternatives (e.g., use search_amazon for keywords, list_bestsellers for category charts, get_amazon_product for single ASIN, google_ai_search for non-Amazon AI search). It also pairs with other tools for downstream analysis, offering complete usage guidance.

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