get_payout_report

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

No annotations are provided, so the description carries the full burden. It discloses the return format (JSON string with specific fields) and adds a caveat that amounts are for analytics, not accounting. This goes beyond a simple 'get' and provides useful behavioral context. However, it doesn't mention side effects (though it's a read operation) or authentication requirements.

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 well-structured: a one-sentence summary, followed by Args and Returns sections. The caveat is included. No wasted words.

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?

Given the tool's simplicity, an output schema exists, and the description covers the return values and note. It also explains parameters sufficiently. The description is complete for an AI agent to invoke it correctly.

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?

The input schema has 0% coverage, but the description compensates with an explicit Args section explaining app_id as an optional filter and period with valid values like '7d', '30d', and date ranges. This fully adds meaning beyond the schema.

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 states the tool gets a payout summary with specific components (gross revenue, Shopify fees, net payout). This distinguishes it from sibling tools like get_revenue_summary, which cover different metrics.

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 doesn't explicitly mention alternatives or when-not-to-use, but the context is clear: it's for payout reports. The parameter details (app_id, period) indicate it's for filtering payout data. No exclusions or sibling comparisons are provided, so a 4.

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