Publish Month Seasonality

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

With no annotations present, the description carries the disclosure burden and it delivers a key behavioral trait: January numbers are inflated because unknown dates were recorded as January 1, and that row is flagged in the output. This prevents an agent from misreading a systematic artifact as real seasonality. It doesn't exhaustively describe every edge case, but covers the behavior most likely to mislead.

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 four sentences, front-loading the purpose before the caveat and alternative. Every sentence adds necessary information and there is no padding or repetition.

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?

The description, together with the output schema and full param coverage, gives an agent everything needed to invoke and interpret the tool. It covers the output's aggregation level, the major data quirk, the flag, and the right sibling for time-series analysis. No critical piece of context 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?

All four parameters have full descriptions in the input schema, so the description does not need to restate their semantics. The description does not add param-specific details, so the baseline score of 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?

The description states the tool's output as coarse month-level publishing seasonality and per-month rating averages. It also distinguishes the tool from stats_by_year by saying the latter is for real time-series work. The resource and scope are clear without ambiguity.

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 explicitly says 'Prefer stats_by_year for real time-series work,' giving a when-not condition and naming the alternative. It also surfaces the January-inflation quirk, so an agent knows to be cautious in month-level interpretation. This is more guidance than most tool descriptions provide.

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