recommend_replenishment

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as recommending a replenishment quantity using forecast and formula, but does not disclose potential side effects (e.g., does it modify data? is it read-only?), whether it requires any authentication beyond the SKU, or what happens with invalid inputs. The description is functional but lacks safety or permission context.

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 a single, front-loaded sentence that efficiently communicates the tool's purpose and method without waste. Every word contributes meaning, making it easy for an AI agent to quickly grasp the tool's function.

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 fully explains the tool's purpose and the parameters are well-documented in the schema (100% coverage). The presence of an output schema compensates for not explaining return values. However, given the complexity of the replenishment recommendation, additional context about assumptions (e.g., demand distribution, formula specifics) or edge cases (e.g., zero stock) could be valuable but is not strictly necessary for basic use.

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 100%, so the baseline is 3. The description adds value by explaining the high-level logic (forecast demand, apply reorder-point/safety-stock formula), which helps the AI agent understand how parameters like 'current_stock', 'service_level', and 'lead_time_days' interact, beyond their individual schema descriptions. This contextualizes the parameters in the overall algorithm.

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 specifies the verb 'recommend' and the resource 'reorder quantity for a SKU', and distinguishes its function by mentioning forecasting demand and applying a reorder-point/safety-stock formula against current stock. This differentiates it from sibling tools like 'forecast_series' or 'classify_demand_pattern' which do not produce a replenishment recommendation.

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 implies the tool is used when a reorder recommendation is needed for a SKU based on demand forecast and stock levels, and the sibling list provides context that other tools exist for forecasting or classification. However, it does not explicitly state when not to use this tool (e.g., when only a forecast is needed, use 'forecast_series' instead) or mention alternatives.

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