gsc_seasonal

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

With no annotations provided, the description fully carries the behavioral burden. It explicitly instructs the agent to base analysis only on returned data, report exact numbers, avoid speculation without evidence, and disclose insufficient data. It also mandates a rich presentation style via artifacts. This is extensive behavioral disclosure beyond structured fields.

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 quite long but well-structured with clear sections (purpose, requirements, analysis rules, presentation). Every sentence adds necessary operational guidance, though the presentation paragraph is verbose. It's not wastefully padded, but could be tightened.

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?

Given the tool has one optional parameter, no output schema, and no annotations, the description is remarkably complete. It covers what the tool does, data prerequisites, how to interpret results, and how to present them. It also addresses edge cases like insufficient data, making it fully contextual for an agent.

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 single parameter 'dataset' has a clear schema description ('BigQuery dataset containing GSC data'), giving 100% coverage. The tool description adds little beyond that, though it mentions the 12+ months requirement which indirectly informs dataset selection. Per rubric, high schema coverage sets a baseline of 3.

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 that the tool performs year-over-year seasonal traffic analysis, listing the specific metrics (clicks, impressions, CTR, position) and the YoY comparison. This distinguishes it from sibling tools by emphasizing the seasonal and long-term BigQuery data requirement.

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 the tool is appropriate: it requires 12+ months of BigQuery data and explicitly notes impossibility with the 16-month GSC API. While it doesn't name alternative sibling tools for other cases, it provides a strong prerequisite and use-case signal.

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