Model score history

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered; the description goes further by disclosing the response composition (per-run scores plus per-axis breakdown for the newest run only). It does not mention the free-vs-paid key restrictions on 'latest', though that lives in the schema.

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 tight sentences: the first front-loads what is returned, the second front-loads the use case. No filler, no repetition of the title.

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?

With no output schema, the description usefully carries the return-shape burden by naming the run-level scores and the per-axis breakdown. It stops short of describing ordering (newest first), listing limits, or how the summary relates to max_points, which the schema covers but the description could tie together.

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 100%, so every parameter (model, period, max_points) is already fully documented in the schema, including the 24h/latest key defaults. The description adds only the loose notion of a 'window', so the 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 — 'Score history of one LLM on AI Stupid Level' — and spells out what the payload contains (each run with its score, plus the newest run's per-axis results). It is unmistakably distinct from siblings like get_leaderboard or get_model.

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?

'Use it to see whether a model got better or worse over time' gives intended usage, but there is no explicit when-not and no named alternative (e.g. use get_leaderboard to compare models rather than track one). Routing guidance is implied rather than stated, leaving the agent to infer the boundary with get_leaderboard/get_model.

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