analyze_product_demand

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

No annotations are provided, so the description carries the full burden. It discloses that estimates are modelled and never measured Amazon sales data, which is a key behavioral trait. However, it does not state whether the operation is read-only, or any other side effects, rate limits, or authentication requirements. The description provides some transparency but lacks completeness given the absence of annotations.

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 a single, well-structured sentence that front-loads the core outputs (demand, trend, seasonality, launch decision) and ends with a crucial caveat about data being modelled. There is no redundancy or fluff; every phrase contributes to understanding the tool's purpose and its limitations.

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?

The description covers the main purpose and output, and hints at the input context (product, marketplace). However, it fails to provide details on parameter formats, whether the marketplace is optional, or how the launch decision is derived. Given the output schema exists (presumably detailing return structure), the description could be more complete about usage constraints but leaves significant gaps for an agent to call it correctly without further probing.

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 description coverage is 0%, so the description must compensate. It only implies product_name via 'for a product' and marketplace via 'on Amazon India', but never explicitly names or explains the parameters. It does not clarify default values or required fields. The description adds minimal value over the schema, leaving agents to infer parameter usage.

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 explicitly states the tool estimates monthly demand, demand level, trend direction, and seasonality for a product on Amazon India, and returns a launch decision. It clearly identifies the verb 'estimate' and the resource, and distinguishes it from sibling tools like analyze_competition or analyze_reviews by focusing on demand and launch decision. The additional note that estimates are modelled, not measured, adds specificity.

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 when to use the tool (for demand estimation and launch decisions) but does not explicitly contrast with alternatives or state when not to use it. There is no mention of prerequisites or when this tool is preferred over others like analyze_purchase_signals or find_product_opportunities. The usage context is only implicitly derived from the description.

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