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

Carries the full burden with no annotations and does well: it discloses that counts include unverified community reports and should be read as relative activity, which materially affects interpretation. It also states ordering ('busiest first'), but omits any note on data freshness or coverage limits of the window.

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?

Three tight sentences: purpose first, use cases second, return shape and caveat last. No filler, and the caveat is placed where it matters for interpretation.

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?

No output schema exists, yet the description still specifies the return shape (retailer plus drop count, sorted descending) and the key data-quality caveat. For a one-parameter read tool, nothing an agent needs to call or interpret it is missing.

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% and the single param documents default 30 and max 90, so the schema does the heavy lifting. The description only refers to 'a window' without adding format or behavior detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource: aggregates Pokemon TCG drop counts per retailer over a time window. The framing ('How many ... each retailer had over a window') implicitly separates it from listing tools like list_recent_drops and snapshot tools like get_in_stock_now, though it never names them.

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?

Gives concrete triggering questions ('who restocks most often', 'which retailer is worth watching'), which is strong positive routing guidance. It does not state when to prefer a sibling such as list_recent_drops for raw events, so exclusions are absent.

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