aurora_forecast

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

No annotations are present, so the description carries the burden. It discloses that the forecast is model-based, fitted and validated on actual data, that the method is disclosed, and that output is either point predictions with an honest horizon or a peak within horizon_hours. Side effects, failure modes, and result structure are not covered, but the core behavior is transparent.

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?

Two sentences with no filler. The core behavior is front-loaded and the usage hint wraps up efficiently; every clause contributes 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?

The description is sufficient for choosing the tool but not fully complete for invoking it: path's meaning is unresolved and the relationship between return_peak and horizon_hours (e.g., whether horizon_hours is required for peak mode) is underspecified. With no output schema or annotations, a bit more detail on expected inputs and return shape would make it complete.

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 0%, so the description must compensate. It adds meaning for return_peak ('just the peak within horizon_hours') and implies horizon_hours controls the forecast window, but the required path parameter is left unexplained. This is a meaningful gap since the agent cannot tell what path refers to.

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 concrete resource and operation: model-based forecast for the run's target column, returning point predictions or a peak. It clearly conveys what the tool does and is distinguishable by function, though it does not name or differentiate against sibling tools like aurora_explain or aurora_simulate.

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?

Provides explicit user intent: 'what will X be / when does it peak' questions, with a directive 'Use for... instead of extrapolating by eye.' This is clear context for when to invoke, but there are no when-not conditions or named alternative tools, so it stops 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.