gsc_forecast

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 discloses that the tool trains a time-series model, requires historical data, and returns confidence intervals. It also adds important guidance on analyzing only returned data and avoiding speculation. It does not explicitly state whether it writes to BigQuery storage, but the 'trains a model' behavior is clearly disclosed.

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, but then includes a lengthy block about analysis and presentation instructions. While these instructions are valuable context for using the output, they are verbose. Every sentence earns its place, but the overall length could be trimmed without losing essential information.

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 complex forecasting tool with no output schema, the description explains what it returns (future clicks, confidence intervals) and gives prerequisites (6+ months historical data). It also provides guidance on how to report results. It does not describe exact output formatting, but that is acceptable given no output schema. Overall, it is complete enough for an AI agent to use effectively.

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 three parameters (dataset, horizon, confidence_level) with descriptions, so schema coverage is 100%. The description does not add any parameter-specific meaning beyond what the schema already provides. Baseline of 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 clearly states the tool's function: 'Forecast organic traffic using BigQuery ML ARIMA_PLUS. Trains a time-series model on your historical click data and projects future clicks with confidence intervals.' This uses a specific verb ('forecast'), names the resource ('organic traffic', 'historical click data'), and distinguishes it from sibling tools like gsc_quick_wins 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 the tool: 'Requires sufficient historical data (ideally 6+ months).' It also notes that this is 'only possible with BigQuery ML,' implying a prerequisite. However, it does not explicitly name alternative tools or specify when not to use it, so it falls short of a 5.

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