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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds useful context beyond that: responses are freshness-stamped, the request costs 1 credit, and a no-result call is not charged. This goes beyond what annotations convey.

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 short sentences convey output contents, filtering, freshness, credit cost, and refund behavior. No filler or repetition; the most actionable information is front-loaded.

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?

There is no output schema, so the description reasonably covers return content (statistics plus samples) and the freshness dimension. It does not mention ordering or sample limits, but the provided context plus schema is sufficient for typical use.

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 documents all parameters with 100% coverage, so the baseline is 3. The description reinforces query as product-name filtering and domain as brand context, but does not introduce significant new parameter-level meaning.

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 names a specific, well-scoped function: retrieving price statistics (min/max/avg/median) and recent product samples for a brand, with an optional product-name filter. This makes the tool's purpose clear and distinguishable from siblings like get_competitor_top_urls or get_brand_snapshot.

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 clear context for when this tool is relevant — pricing data for a brand, optionally narrowed by product name. It does not explicitly compare against sibling tools, but the domain is specific enough that an agent can infer appropriate use.

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