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commerce_revenue_by_product

Destructive

Retrieve revenue figures grouped by product to support sales analysis and reporting.

Instructions

Run the commerce domain agent action revenue_by_product.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputsNo{}
messageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

The annotations already carry the destructive/non-read-only safety profile, and the description adds useful context that calls route through the domain-agent dispatcher under the caller's JWT, tenant, and company scope. It does not add detail about side effects or external data use, but it does not contradict the annotations.

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 compact and front-loaded: one clear action statement, one routing sentence, and two scannable argument bullets. Every part earns its place with no redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-string-parameter wrapper with an output schema, this is close to sufficient: an agent can infer the call shape from the argument roles. It still omits usage context, structured-input examples, and any behavioral caution beyond the annotations, which is a notable gap given the large sibling set.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description gives both arguments a semantic role—`message` is a free-text objective and `inputs` is an optional JSON string—which the schema itself lacks. However, with 0% schema description coverage, it only minimally compensates: it doesn't show what keys `inputs` may contain or how `message` should be phrased.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence identifies a concrete action (`revenue_by_product`) and frames it as a commerce domain-agent action, so the target is clear. It relies on the tool name for what the action computes and does not explicitly contrast it with siblings like `commerce_revenue_by_channel`, but it is still specific about the verb and resource.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance states when this tool should be used instead of the many commerce_* siblings or `dispatch_domain_agent`. The description explains routing mechanics but leaves selection entirely to inference from the tool name.

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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