GoAI Moat Cross-Border Returns & Refund Management

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.

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

With no annotations, the description carries the full burden. It discloses that the output is a targeted action checklist plus generic advice, and it adds an important trust caveat that the checklist is industry-experience-based and must be self-validated, not a platform-hard standard. It does not discuss side effects, but as a read-only advisory tool that is a minor 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?

The description is compact and well-structured: purpose line, parameter section, return section, and caveat. Every sentence carries distinct information with no filler, and the core behavior is front-loaded.

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, low-complexity tool, the description covers the operation, the allowed input, the return shape, and the reliability caveat. It is nearly complete, but it omits any guidance on how this tool relates to return_policy_playbook or calculate_return_cost, which would help an agent choose among the siblings.

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 compensate; it does by listing all five return_reason values inline. It connects the values to the Chinese labels in the first line, which adds semantic meaning the bare schema lacks, though it does not define each enum value individually or restate the default.

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 names a specific action ('按退货主因给针对性降退货率清单' – produce a targeted return-reduction checklist by return reason) and enumerates the five reason categories, so an agent can tell what the tool does. It does not explicitly differentiate it from sibling tools calculate_return_cost or return_policy_playbook, so it stops short of a 5.

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 implies the tool is for generating return-reduction action items for a given return_reason, and the return_reason options define the context. It gives no explicit 'use when / use instead' guidance or contrast with sibling tools, leaving some inference to the agent.

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

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

Annotations are none, so the description carries the full burden. It discloses that the policy is based on public information as of a certain date and must be self-verified (未 '截至2026须自验'), and that it does not fetch real-time data (不实时抓取). This is valuable transparency about staleness and non-dynamic behavior. It doesn't mention whether it is read-only or any side effects, but as an informational tool, that's less critical. Given the explicit caveats, a 3 is reasonable; higher would require more detail on data source limitations or error handling.

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 compact and front-loaded: it starts with the core purpose, then details parameters and return content, and ends with important caveats. Every sentence adds value without fluff. The structure is logical (purpose → params → return → caveats), making it easy to scan.

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 complexity (multiple platforms, multiple policy aspects) and that annotations are none, the description covers the essential: platforms, parameter values, what is returned, and data staleness caveat. An output schema exists, but the description does not rely on it; it explains the return content (return window, cost structure, etc.) without needing the output schema to fill gaps. Missing details might include specific examples or error handling for invalid platform names, but for an informational tool, this is nearly complete. A 5 would require explicit mention of what happens with invalid input or more detailed return structure.

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 explain the parameter. It does: it lists the platform parameter with allowed values (amazon/walmart/ebay/tiktok_shop/shopify_dtc) and notes case insensitivity. It also explains that the return includes the platform's return window, cost structure, returnless support, and management points. This adds meaning beyond the bare schema, which only has a type and default. A higher score might include examples or default behavior clarification, but this is already strong.

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 clear purpose: comparing return policies across specific platforms (Amazon/Walmart/eBay/TikTok Shop/Shopify DTC) and providing return management points. It is specific about the resource (return policies) and the scope (platforms). While it doesn't explicitly name sibling tools, the focus on policy comparison and management guidance distinguishes it from cost calculation (calculate_return_cost) and reduction strategies (reduce_returns_playbook). A higher score would require explicit contrast with siblings.

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 implies when to use this tool: when you need return policy details for a platform. It doesn't explicitly state when NOT to use it or suggest alternatives. Sibling tools like calculate_return_cost and reduce_returns_playbook are not mentioned, so an agent might not know this is the policy reference tool rather than a cost or reduction tool. The usage context is clear but lacks explicit routing guidance, so a 3 is appropriate.

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