forecast_7day_shap

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'honest caveats' and 'calibrated probability' but does not specify what caveats entail, whether the tool is read-only, or any prerequisites (e.g., a forecast must already exist). This is adequate but vague.

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-loading the purpose and then the usage instruction. Every word earns its place; no redundancy or fluff.

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 single-parameter explanation tool without output schema, the description covers the core purpose and usage. However, it lacks details on return structure and specific caveats, which would improve completeness. Still, it is largely sufficient given low complexity.

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% with a clear description for 'country_code'. The tool description adds no additional information about the parameter, so it meets the baseline but does not exceed it.

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 provides SHAP explanations for a 7-day shutdown-risk forecast, showing per-feature contributions with caveats. It explicitly mentions the verb 'understand' and the resource 'forecast of a country', which distinguishes it from sibling tools like forecast_tft or forecast_multi_horizon that produce forecasts rather than explanations.

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 second sentence states 'Use to understand WHY the forecast is high or low', giving clear usage guidance. However, it does not explicitly mention when not to use it or contrast with alternative tools (e.g., raw forecast tools), which would strengthen the guidance.

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