kaggle_leaderboard_track

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is safe and non-destructive. The description adds valuable behavioral context: the tool is stateful (stores snapshots locally), requires sequential calls to compute meaningful deltas, and only works on the public leaderboard. This goes beyond the annotations by explaining the stateful, snapshot-based behavior.

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 only two sentences, yet it conveys the purpose, unique value, limitations, and output details. It is front-loaded with the core functionality and uses efficient phrasing. Every sentence contributes essential information without redundancy.

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?

Given the low parameter count, existence of an output schema, and annotations covering safety, the description is fairly complete. It explains what the output contains (rank deltas, new entrants, etc.) but does not detail how snapshots are managed or how many snapshots can be stored. The stateful nature is mentioned, but the agent might need additional hints about persistence or resetting.

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 has 0% description coverage, so the description must compensate. It explains the role of 'top_n' and 'your_team' explicitly (e.g., 'your movement + who passed you'), but does not fully describe the format or constraints (e.g., top_n default is 20, your_team is an optional string). The description adds some meaning beyond the schema but leaves gaps for the agent to infer.

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 states the tool's purpose: snapshot the public top-N leaderboard and compute deltas relative to the last snapshot. It explains what the output includes (rank deltas, new entrants, climbers, your team movement). It also differentiates the tool from a simple leaderboard query by noting the stateful tracking and lack of historical endpoint on Kaggle.

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 specifies when to use this tool: for tracking leaderboard changes over time using snapshots. It explicitly states that it only works for the public leaderboard and that deltas are against your previous snapshot, not an absolute time series. However, it does not explicitly exclude use cases where an absolute time series or private leaderboard data is needed, nor does it name sibling alternatives like kaggle_competition_leaderboard directly.

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