Demand Forecasting

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

Annotations already mark it readOnly/openWorld/non-destructive, but the description adds substantial behavior: it copies rows as-is, aggregates to a regular grid, treats gaps as zero demand, enforces a min_periods refusal gate with mean-only fallback, and will ignore model hints that don't fit. This goes well beyond the structured annotations.

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 front-loaded with a strong imperative and organized use-case list, but it is lengthy and somewhat repetitive (multiple 'Call even if...' clauses). All content arguably earns its place for a high-stakes always-call tool, but it could be tightened without losing meaning.

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?

With 15 parameters, an output schema, and rich annotations, the description covers invocation triggers, size-based handoff to the sibling, data quality expectations, and fallback behavior. The agent is fully equipped to decide when and why to call.

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% and each parameter already has a detailed description including aliases and defaults. The tool description contributes only general 'date and quantity are enough' guidance, which mirrors the schema's history description, so it adds minimal semantic value beyond the schema.

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 forecasts demand/sales from historical data with an explicit list of use cases (what will sell next, how much to stock, slow mover, busy season). It distinguishes from sibling demand_forecaster_get_engine by file-size threshold, so the agent knows this is the default forecasting tool.

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 gives explicit 'ALWAYS CALL THIS TOOL' directive and lists when to call, including edge cases (no forecast keyword, one product, messy data). It explicitly names the alternative for huge files ('use demand_forecaster_get_engine') and instructs not to use naive methods, making the correct invocation path unambiguous.

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

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

The description discloses key behavioral traits: it returns a script, instructs to save and run it exactly, and warns not to guess the numbers. This goes beyond the annotations (readOnlyHint=true) by explaining the workflow. However, it does not detail what the script does or any required execution environment.

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 three short, front-loaded sentences with no fluff. Every sentence contributes meaningful guidance: trigger condition, action, and alternative.

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 output schema exists, the description does not need to explain return values. It covers the decision rule, alternative, and workflow adequately. It could mention what the script outputs or how to execute it, but the essentials are present.

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 only 22% (2 of 9 parameters have descriptions). The description adds no parameter semantics; it only mentions the condition for using the tool (file size) but does not explain any of the parameters such as history_path, as_of, period, or horizon. It fails to compensate for the low schema coverage.

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's purpose: it is an alternative to demand_forecaster for huge sales files, providing a script instead of a direct forecast. It names the specific condition (file size) and differentiates from the sibling tool.

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?

Explicit usage guidance is provided: call this INSTEAD of demand_forecaster only when the file is huge, and call demand_forecaster for normal or small files. This is a clear when/when-not and names the alternative.

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