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

With no annotations provided, the description carries the behavioral disclosure burden. It adds useful constraints: daily granularity, a configurable `days` window, a maximum of 1000 days, and the February 2024 data start. It does not discuss output structure or edge cases, but an output schema exists to cover return values.

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?

Two concise sentences with important constraints and the intended use case placed early. There is no filler, and every sentence adds value.

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 gives the key constraints, data availability window, and intended use case, while the output schema handles return-value details. It is slightly incomplete on how `product_id` maps to 'printing' and on explicit alternatives for non-trend price questions.

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 provides meaningful semantics for `days` (max 1000 and history start date), but `product_id` is only implied by 'per printing' and is not explained in terms of how to identify a product or printing. This is partial but not complete compensation.

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 returning daily market price history per printing over a configurable window. It adds a use case ('trend questions: up or down, since when'), which distinguishes it from current-price siblings, but it never names an alternative tool explicitly.

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 explicitly tells the agent when to use it: for trend questions such as whether prices are up or down and since when. It does not explicitly state when not to use it or point to sibling tools like card_price for current price queries.

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