extract_listing_improvements

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 the AI model (claude-opus-4-7), cost range, required API key, and output structure (title, bullets, etc.), which is good transparency. However, it does not mention latency or potential failure modes.

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 relatively concise, with two paragraphs that front-load the core value. Minor redundancy exists (e.g., 'grounded in actual customer language' and 'citing the pain point').

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 tool's complexity (AI model, cost, API key), the description covers what it does, input (implicitly ASIN/market/limit), output format, and prerequisites. It lacks detail on how to obtain the VOC report and does not provide an output schema, but the output structure is described in text.

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 has three parameters (asin, market, limit) with no descriptions (0% coverage). The description does not explain these parameters at all, so the agent gains no additional meaning 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 identifies the tool as a 'differentiator' that produces specific listing improvements from a VOC report, distinguishing it from raw data tools like 'analyze_reviews'. However, it does not explicitly contrast with each sibling tool, leaving some ambiguity.

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?

It provides context by contrasting with 'Data Dive style' and mentions API key and cost, implying usage when actionable suggestions are needed. But it lacks explicit 'when not to use' guidance or explicit alternatives among siblings.

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