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

Goes well beyond the readOnly/idempotent annotations: discloses aggregation of the daily series, the >= 2 full cycles threshold for seasonality (otherwise 1.0 stated in the response), model selection by adjusted R², interval construction (±1.96 × residual sd, widening with horizon), and the failure condition (fewer than 8 full weeks / 6 full months returns an error). That is exactly the behavioral context an agent needs before invoking.

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?

Front-loaded with the core action and method, then output fields, error conditions, and usage framing in a logical order. It is dense and somewhat overstuffed with parentheticals, and the closing Portuguese sentence duplicates content already stated in English.

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 17-parameter tool with no output schema, the description still names the returned forecast fields (baseline, low/high, seasonalIndex, growth scenario) and the error path. Combined with full schema coverage, an agent has everything needed to call it correctly and interpret the result.

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 coverage is 100%, so the baseline is 3, but the description adds real meaning: horizon is in weeks or months depending on granularity, model=auto means best adjusted R², cvr+aov combine into revenue (visits × cvr × aov), and growthRate applies baseline × (1 + growthRate). Filter parameters (query, country, brand) are left entirely to the schema, so not a 5.

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 precise verb+resource: projecting clicks or impressions for a property over a horizon, with the defaults (12 weeks / 6 months, 364-day history) spelled out. This is clearly distinguishable from siblings like get_timeseries (historical) or detect_anomalies (retrospective).

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?

Gives explicit positive contexts ('what should we expect next quarter', target setting, scenario planning) and a caveat framing it as trend extrapolation, not a promise. It does not name alternatives (e.g., get_timeseries for actuals) or state 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.