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

Annotations already declare readOnly, idempotent, non-destructive, so the safe-read profile is covered. The description still adds real behavioral context the annotations cannot: the tool fails or is irrelevant unless a store is connected, and listWorkspaces tells you which workspaces qualify.

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 short sentences, front-loaded with what it returns and then the access constraint. Every clause carries information an agent needs; nothing is filler.

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?

Output schema exists, so return-shape explanation is unnecessary, and the description covers purpose and the key gating condition. What remains thin is the relationship to adjacent sales tools (trend, top products, store overview), which an agent must infer from names.

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 all four parameters (days, from/to, workspaceId) are already fully documented including enum values and date-range constraints. The description adds only the implication that results are grouped by channel, not any parameter-level guidance beyond the schema. Baseline 3 applies.

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 the concrete metrics returned (orders, revenue, units) and the grouping dimension (per connected channel), which is more specific than the tool name alone. It does not explicitly distinguish itself from close siblings like readSalesTrend or readStoreOverview, so the boundary is inferred rather than stated.

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?

Gives concrete user-intent examples ('which channel sells more', 'Shopify vs WooCommerce') that map directly to when to call it, plus an explicit precondition and exclusion: only for workspaces with a store connected. It stops short of naming a sibling as the alternative for non-store workspaces or for trend-over-time questions.

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