forecast_series

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

With no annotations, the description carries full behavioral burden. It reveals the internal routing logic (mean fallback, TSB, AutoETS) but does not mention authorization needs, error behavior, or operational constraints such as required permissions or data prerequisites.

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 concise with two sentences. The first sentence states the core purpose, and the second elaborates on the internal routing. Every word serves a purpose, no redundancy.

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?

Given the tool's moderate complexity, an output schema exists, and parameters are well-covered in the schema, the description covers the essential algorithmic behavior. It could mention prerequisites (e.g., valid SKU) or edge cases, but overall it is reasonably 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 coverage is 100%, so baseline is 3. The description adds no additional meaning to parameters beyond what the schema already provides (horizon default/description, unique_id description with pointer to list_skus). The description does not elaborate on parameter usage or constraints.

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 verb 'Forecast future demand' and the resource 'a SKU'. It distinguishes from siblings by explaining the automatic classification and routing to different models, making the unique purpose evident.

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 implicitly indicates when to use this tool (to forecast a SKU's demand) but does not explicitly state when not to use it or suggest alternatives like classify_demand_pattern or evaluate_forecast for related tasks. The context is clear but lacks exclusion guidance.

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