Stats By Year

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

With no annotations, the description carries the behavioral disclosure burden. It usefully discloses that results are always ordered chronologically and that publish_year is the only reliable temporal field. It does not describe truncation behavior with limit or what happens when a year has no data, but the output schema and parameter descriptions cover much of the remaining contract.

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 definition is compact and front-loaded. The main purpose is in the first sentence, and the follow-up sentences add high-value guidance about temporal reliability and ordering without waste.

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?

For a read-only aggregation tool with 100% schema coverage and an output schema, this definition is substantially complete. It provides the central aggregation, the ordering contract, and the key data-quality caveat. Minor gaps like limit truncation direction are not enough to make it incomplete.

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 the baseline is 3. The description adds a useful domain note about publish_year reliability that is relevant to year_from/year_to, but does not provide per-parameter semantics beyond what the schema already states.

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 opening phrase clearly identifies the resource—rating statistics and publication volume—and the grouping by publication year. It stops short of a full verb phrase like 'Returns' or 'Computes', but it does convey the tool's core purpose. The publish_year/publish_month caveat also helps distinguish this from the publish_month_seasonality sibling.

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 gives clear context for when this tool is appropriate: any real time series should use publish_year rather than publish_month. This implicitly warns against the seasonality sibling and sets expectations for time-series use. It does not explicitly name alternative tools or spell out when to prefer stats_by_language, stats_by_publisher, etc.

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