Estimate a SKU’s sales sensitivity to price from 52 weeks of data, returning elasticity, a 95% confidence interval, R², and a confidence label.
Estimate how sensitive a SKU's unit sales are to price, from 52 weeks of sales.
Returns the elasticity, a 95% confidence interval, R², and a confidence label.
| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, closed-world behavior; the description adds the meaningful trait that the estimate is derived from a 52-week historical window and that it is a statistical output with a confidence interval and confidence label. It does not say what happens when a SKU has insufficient history, which is the main remaining behavioral gap.
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?
Two sentences, purpose front-loaded, nothing redundant. The second sentence on returns is arguably covered by the output schema, but it is short and useful.
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 read-only tool with an output schema, the description covers what is computed, the data window, and the shape of the result. It stops short of stating data-sufficiency requirements or SKU-level scope, which matters for a statistical estimator.
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?
Only one parameter exists and schema description coverage is 0%, so the description must carry the burden. It implies the input is a SKU ('a SKU's unit sales') but adds no format, identifier convention, or granularity (per channel? per region?) beyond the property name 'sku'.
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?
States a specific verb (estimate) and resource (price elasticity / sensitivity of unit sales to price) with the data basis (52 weeks of sales). It is distinguishable from siblings like simulate_price_change by framing itself as an estimation from historical data rather than a forward simulation, though it never names or contrasts those siblings explicitly.
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?
Usage is only implied: the 'from 52 weeks of sales' framing suggests this is the historical-measurement tool and not the what-if tool, but there is no explicit when-to-use, when-not-to-use, or reference to simulate_price_change / check_price_guardrails as alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.