gsc_quick_wins

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states analysis rules: 'Base your analysis ONLY on the data returned', 'Report exact numbers', 'Do not speculate about causes', and 'If the data does not contain enough information to answer a question, say so clearly.' It also details the presentation format, including artifacts, summary cards, and color-coded indicators, which goes beyond the schema and adds significant behavioral context.

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 is front-loaded with the core purpose, followed by important analysis rules and presentation requirements. While it is longer than the two-sentence ideal, every sentence adds necessary instructions for tool behavior and output format. The structure is logical and avoids redundancy, making it appropriately detailed for the tool's complexity.

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?

The description covers the tool's purpose, analysis constraints, and output presentation in detail, which is crucial given the lack of an output schema and annotations. However, it does not explicitly list the fields or metrics returned (e.g., keyword, impressions, position), leaving a minor gap in understanding the exact output structure. Overall, it is largely complete for a 4-parameter tool with clear scope.

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 schema description coverage is 100%, so the baseline is 3. The description does not add significant meaning beyond the schema for individual parameters, but it does map the core concept of 'positions 4 to 15' to max_position and 'high impressions' to min_impressions. This alignment reinforces the tool's purpose without adding technical details that the schema already provides.

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 clearly states the tool's function: 'Find keywords from GSC bulk export data at positions 4 to 15 with high impressions' and identifies them as 'striking distance keywords that could be pushed to page one.' This uses a specific verb, resource, and scope, effectively distinguishing it from sibling tools like gsc_content_gaps or gsc_traffic_drops.

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 to use this tool: when targeting keywords in positions 4-15 with high impressions to identify quick wins. However, it does not explicitly mention alternatives or exclusions, such as when to use gsc_ctr_opportunities or gsc_content_recommendations instead. The context is sufficient for an informed agent, but lacks explicit guidance on not using it.

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