Rank-tracking time series

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

Annotations already declare the safe read-only, idempotent, non-destructive profile, so the bar is lower. The description adds genuinely useful behavioral context beyond that: a data-retention constraint on the date range and a response-size tradeoff tied to granularity. It doesn't cover return shape or pagination, keeping it off a 5.

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?

Dense and front-loaded: the core purpose leads, followed by the size advice, retention constraint, and illustrative questions. Every sentence carries information, though the example-questions clause is a slight luxury.

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 10-parameter tool with no output schema and only 40% schema coverage, the description is adequate but leaves notable gaps — several filtering parameters are undocumented here and in the schema. The safety profile is covered by annotations, but parameter-level completeness is not.

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 only 40% across 10 parameters, so the description is expected to compensate — but it only touches granularity (already an enum) and the optional competitor lines. The engines, countries, queryClusterIds, and includeUngroupedQueries parameters receive no added meaning in the description.

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 — 'Time series of rank-tracking metrics for a project across a date window' — and specifies the bucketing dimension. This cleanly distinguishes it from the sibling get_tracked_query_time_series (query-level) and the point-in-time get_project_rank_tracking_stats.

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?

Gives concrete usage context via example questions ('what changed in my AI visibility', 'share-of-voice trend split by engine') and a scoping rule ('Prefer weekly or monthly for long windows to keep the response compact'). It does not explicitly name alternatives to use instead for query-level or aggregate views, so it stops short of full when/when-not guidance.

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