gsc_ctr_opportunities

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

With no annotations, the description carries the behavior burden and does so well. It explicitly states to base analysis only on returned data, report exact numbers, avoid speculation unsupported by data, and honestly state when information is insufficient. It also mandates a specific presentation format (rich interactive visualization with artifacts). This gives meaningful behavioral transparency beyond the basic read-only nature.

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 front-loads the core purpose in the first sentence, then separates analysis rules and presentation requirements into clearly labeled sections. While long, each section provides necessary behavioral guidance. It is somewhat verbose but every part adds practical value for correct usage.

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?

There is no output schema, so the description compensates by specifying how results should be presented and how the agent should reason about them. It clearly defines the analysis approach and output format. It could be more complete by mentioning when to choose this tool over related siblings, but for the tool's complexity it is sufficiently complete.

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?

The input schema covers all 3 parameters with descriptions (100% coverage), so the description adds no parameter-specific semantics. The baseline of 3 applies because the schema already documents each parameter (days, dataset, min_impressions) with defaults and thresholds. The description does not repeat or enrich these, but no extra burden is needed.

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 uses a specific verb ('Find') and resource ('pages') with a clear qualifier: 'high impressions but CTR significantly below the expected benchmark for their ranking position.' It also names the practical output ('title and meta description optimisation candidates'), which distinguishes it from siblings like gsc_ctr_benchmark that likely just report benchmarks.

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 clearly frames the tool's purpose as identifying optimization candidates, implying when to use it (when looking for CTR/title/meta opportunities). It does not explicitly name alternatives or exclusions, but the user intent is evident. The 'IMPORTANT' instructions also clarify how the tool should be used for analysis, which adds guidance.

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