GoAI Moat Cross-Border Pricing Strategy

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

With no annotations, the description carries the burden of explaining behavior, and it does disclose the main pipeline: banding, margin check, and positioning advice. However, it does not mention that the margin check is skipped when cost is 0, and it offers no caveats or limitations; that conditional behavior is only documented in the parameter schema.

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 a single sentence with no filler. It front-loads the core action and includes the tier names, followed by the two outputs, making it highly efficient for an agent to read.

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?

The output schema exists and the parameters are well documented, so the description does not need to explain return values or parameter formats. However, it lacks guidance on when to choose this tool over its siblings and hides the conditional margin-check behavior in the schema, leaving the context incomplete for an agent deciding how to invoke it.

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 three parameters are already documented in the input schema. The description adds no additional meaning about the parameters; it only restates the general goal, so the baseline score of 3 is appropriate.

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 what the tool does: it places your price into a four-tier competitor band and performs a gross-margin check plus positioning recommendation. It is specific about the resource and action, but it does not explicitly differentiate itself from the sibling tools price_psychology_tactics and recommend_pricing_strategy.

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?

No guidance is given about when to use this tool versus its siblings. The description implies use when you have competitor prices and your price, but it does not state exclusions, prerequisites, or alternatives, so an agent gets little help choosing among competitive_price_positioning, price_psychology_tactics, and recommend_pricing_strategy.

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?

No annotations are provided, so the description carries the full burden. It accurately states that the tool returns a dict with six categories and a disclaimer, and explicitly notes it takes no parameters. However, it does not explicitly disclose that this is a read-only, side-effect-free operation, though this is strongly implied by 'returns'. It is adequate but not richly informative about behavior beyond the return structure.

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 two sentences of minimal length. The main purpose is front-loaded, and the second sentence specifies the return format. There is no wasted wording, and it is appropriately sized for a no-parameter 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 zero-parameter tool with an output schema present, the description is complete: it states what is returned (six tactic types plus a disclaimer) and that no parameters are needed. It doesn't explain the detailed structure of the return, but the output schema covers that. The description is sufficient for an agent to call it correctly and know what to expect.

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 tool has zero parameters, so the baseline is 4. The description reiterates '无参数' (no parameters), which is redundant with the schema but harmless. Since there are no parameters to explain, the description does not need to add meaning; the schema already covers everything.

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 it returns a library of 6 types of psychological pricing tactics, each with principle, when to use, and example. This is a specific verb-resource combination that distinguishes it from siblings like competitive_price_positioning and recommend_pricing_strategy, which focus on positioning and strategy recommendation respectively.

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 mentions that each tactic includes 'when to use' information, but this refers to the content of the returned library, not when to call this tool versus alternatives. There is no explicit guidance on when to use this tool instead of the sibling tools, and no exclusions or alternative recommendations.

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 of behavioral disclosure. It does this well by explaining the internal logic: cost-plus as the floor, value-based as the ceiling, leading to a recommended range plus strategy name and reason. It does not describe side effects or assumptions, but for a non-mutating recommend tool this is reasonably transparent.

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?

A single compact sentence conveys the full purpose, method, and output shape using arrows, with no filler. It is front-loaded and every phrase earns its place.

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?

The tool is relatively simple, the input schema is fully self-describing, and an output schema exists, so return values do not need to be described. The description covers the methodology and deliverable. The main completeness gap is the absence of any guidance about when to choose this tool over its siblings, but that is a usage concern rather than a contextual gap in calling 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics by mapping cost to the cost-plus floor and competitor_price to the value-based ceiling, which clarifies how the two required parameters are used together. This goes beyond the schema's individual field descriptions.

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, '推荐定价策略' (recommend pricing strategy), for a specific resource, a cross-border product, and outlines the deliverable: a price range, strategy name, and rationale. It is clear and not tautological, but it does not explicitly distinguish itself from the sibling tools competitive_price_positioning or price_psychology_tactics.

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 used when a cross-border product needs a pricing strategy based on cost and competitor price, but it never explicitly states when to use it versus the sibling tools. There is no when-not-to-use guidance or mention of alternatives, so usage context is only implied.

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