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

With no annotations, the description carries the full disclosure burden. It does add real behavioral context: costing zero credits and being sourced from SERP checks rather than live queries, plus a cap of 100 rows with rows_total returned. It omits permission/auth requirements, whether it reads or triggers checks, and caching/freshness behavior, so it is informative but incomplete for an unannotated tool.

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?

It is a single dense sentence that front-loads the purpose ('Impatto delle AI Overview sulle keyword monitorate') before enumerating return contents. Every clause carries information, though the long list of return fields makes it heavier than ideal and partly overlaps with what an output schema would normally provide. No filler 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?

With no annotations and no output schema, the description reasonably compensates by enumerating the result contents and noting the row cap. Still missing are prerequisites (must SERP checks already exist for the project?), what 'monitorate' keywords means, and any freshness caveat. It is adequate but not fully self-sufficient for a data-analysis tool.

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 coverage is 50%: project_id is documented in the schema ('ID progetto (da list_projects)') and limit is only bounded by min/max. The description adds meaning by stating the output is capped at 'max 100 righe + rows_total', which explains the limit's effect, and its parenthetical points the agent toward list_projects for the project_id. It does not explain limit's unit or default beyond the schema's maximum, so a baseline 3 is appropriate.

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 names a specific analysis (AI Overview impact on monitored keywords) and enumerates the concrete outputs: how many have AIO, how many cite the site, at-risk keywords, estimated traffic at risk, cited domains, and delta vs the previous control. This is far more specific than a tautology and lets an agent distinguish it from generic siblings like get_ai_visibility or get_ai_competitors. It lacks an explicit contrast statement against those siblings, so it stops short of a 5.

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?

It gives implied usage context by noting 'zero crediti' and that the data comes 'dai check SERP', which tells the agent this is a free, derived-data read rather than a freshly charged analysis. However, there is no explicit when-to-use/when-not guidance and no named alternative (e.g., when to prefer run_ai_visibility_check or get_ai_visibility). Usage is inferable but not spelled out.

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