forecast_sales

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

With no annotations provided, the description carries the full burden and does well: it reveals auto-backtesting across 9 models, MAPE-based recommendation, and the output content (per-period forecasts plus accuracy table). This is meaningful behavioral context beyond the input schema.

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 two sentences, front-loads the core purpose, and packs in substantial detail without redundancy. The model list is long but earns its place by clarifying scope and methodology.

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 forecasting tool with no output schema, the description covers purpose, method, model evaluation, and return contents well. It does not mention data requirements or failure modes, but given the schema documents all parameters and everything is optional, the remaining gaps are minor.

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 description coverage is 100%, so the parameters are already well documented. The description adds no additional meaning beyond naming sales measures that match the schema's 'measure' values, which is not enough to raise the score above baseline.

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 names a specific verb ('Predict'), a clear resource ('future sales revenue, quantity, or gross profit'), and enumerates the statistical modeling approach. It is easily distinguishable from forecasting siblings like forecast_cash_flow and forecast_item_demand because it explicitly scopes to sales metrics.

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 implies the tool is for sales forecasting but never explicitly states when to use it over alternatives such as forecast_item_demand or detect_seasonality. There is useful context but no explicit when-to-use/when-not-to-use guidance.

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