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 does this well: it discloses the exact cost caliber/formula (refund management fee $0.30, return processing fee by size tier, disposal fee, residual value loss), distinguishes FBA vs FBM cost structures, and explicitly flags all figures as '截至 2026 须自验' (must self-verify as of 2026). For a pure calculation tool with no side effects, this is strong transparency.

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 long but every sentence earns its place given 7 parameters and 0% schema coverage. It is front-loaded with the purpose statement before diving into parameters, and the cost-caliber section is dense with necessary methodology. Slightly verbose, but the density justifies the length for a complex financial calculation tool.

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 7-parameter calculation tool with 0% schema coverage, the description is complete: it covers all inputs with semantics, enumerates the four outputs, documents the cost methodology, and flags data-validity caveats. The output schema exists to formalize return values, so the prose need not duplicate that. Nothing an agent needs to invoke this correctly is missing.

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 0%, so the description must fully compensate – and it does. All 7 parameters receive semantic context beyond their schema definitions: avg_price gets units (USD), return_rate_pct gets typical category ranges (15-20% apparel, 5-10% standard), restock_rate_pct gets the FBA ~68% measured caliber, and is_fba/returnless_refund get cost-behavior explanations. This is exemplary compensation for a zero-coverage 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 states a specific verb+resource ('退货真实成本核算' – return cost accounting) and enumerates four concrete outputs: per-unit cost, monthly cost, gross-profit erosion, and returnless viability. This clearly distinguishes it from siblings reduce_returns_playbook and return_policy_playbook, which target reduction and policy rather than cost calculation. An agent can immediately tell what this tool does and what it is not.

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 context is implied through the verb '核算' (calculate/account) versus the siblings' 'reduce' and 'policy' focus, but there is no explicit statement of when to choose this tool over the alternatives or any exclusions. The description tells the agent what it computes but never says 'use this when you need cost figures; use reduce_returns_playbook when you need reduction tactics.' The differentiation is inferable, not stated.

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