Demand Forecasting

ONLY for a huge sales file — get the script

demand_forecaster_get_engine

Read-only

Call this INSTEAD of demand_forecaster only when the file is huge (more than about 2500 rows or 200 products). It gives a script. Save it exactly and run it. Do not guess the numbers. For a normal question or a small file, call demand_forecaster.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`period`	No		W
`horizon`	No
`meta_path`	No	Optional path to a per-product file with lead_time_days.
`min_periods`	No
`history_path`	No	Path to the sales history file (CSV/TSV/JSON/JSONL) inside your sandbox. May also carry a lead-time column.
`seasonal_periods`	No
`interval_confidence`	No
`default_lead_time_days`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`notes`	No
`period`	No
`script`	Yes
`horizon`	No
`language`	No
`requires`	No
`writes_file`	No
`instructions`	Yes
`code_integrity`	Yes
`engine_version`	Yes
`meta_path_used`	No
`data_requirements`	No
`history_path_used`	No
`network_access_required`	No

TDQS

A4.1/5.0

Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters1/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

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TDQS

A4.3/5.0

Disambiguation5/5

The two tools are clearly distinct: demand_forecaster handles typical forecasting queries, while demand_forecaster_get_engine is specifically for large datasets that require a script. Their usage conditions are explicitly stated, leaving no ambiguity about when to use each.

Naming Consistency5/5

Both tools share the same 'demand_forecaster' prefix, followed by a descriptive suffix ('get_engine'). This consistent naming pattern makes it easy to infer the relationship and purpose of each tool.

Tool Count3/5

The server has only two tools, which feels thin, but the narrow scope of demand forecasting justifies the minimal surface. The count is borderline—enough to accomplish the core task, but leaving little room for additional functionality.

Completeness4/5

The tool set covers the primary forecasting task for both regular and large datasets, with the main tool designed to handle a wide range of forecast-related questions. Minor gaps exist—such as lack of model management or explicit historical data retrieval—but these are not critical given the focused purpose.

Resources

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