list_trend_top

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

No annotations are provided, so the description carries the full burden, yet it says nothing about how 'heat' is computed, tie-breaking, stability of ranking, or truncation behavior at the boundary. Only the ordering criterion is named, with no explanation of it.

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?

A single short sentence with no padding and the ordering criterion front-loaded, which is efficient — but its brevity comes at the cost of under-specification rather than tightness.

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?

An output schema exists, so return values need not be explained, but for a listing tool the description still fails to identify what entity is being ranked or how the ranking is derived — the two things an agent most needs before calling it.

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 0% and the schema only says 'N' with a default of 20, so the description must compensate. It does partially by clarifying that n is a count of top items and that ranking is by heat, but it omits bounds, maximums, and behavior for n larger than the available set.

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 ('return') and quantifier ('top n by heat'), but the resource itself is left as an unqualified '條' (items/entries) — an agent cannot tell whether these are videos, trends, reports, or clips without guessing from the tool name. Adjacent siblings like list_videos make this ambiguity material.

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?

There is no guidance on when to prefer this over list_videos or get_report, no prerequisites, and no note on what happens when fewer than n items exist. Usage is only inferrable from the name.

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