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

Annotations already declare readOnly=true, idempotent=true, destructive=false, so the safety profile is covered. The description usefully discloses the shape of the result (clusters with total clicks, impressions, example queries), which matters since there is no output schema, but says nothing about the clustering method or the default 90-day window.

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 tightly written sentences with the core action front-loaded and no filler. Nothing is wasted, though it is arguably too terse given the undocumented parameters.

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 4-parameter read tool with no output schema and 25% coverage, the description covers purpose and output shape but leaves three parameters unexplained. Adequate but with clear gaps an agent would have to guess at.

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 25% – just 'property' is documented. The description adds no meaning for days, limit, or minQueries, and notably never explains that minQueries controls the minimum cluster size. With low coverage the description should compensate, and it does not.

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 ('groups queries by shared terms into topic clusters') and even names the return contents (clicks, impressions, example queries). It distinguishes itself from the top-query listers by being a clustering tool, though it never explicitly contrasts with siblings like gsc_top_queries or gsc_question_queries.

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 closing clause ('where a hub page or new content would pay off') implies a content-strategy use case but gives no explicit when-to-use, when-not-to-use, or named alternative. An agent must infer selection from the purpose alone.

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