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GoAI Moat Cross-Border Cost & Profit

Breakeven Price

breakeven_price

反推保本价与达到目标净利率所需的售价。

解方程:售价 × (1 − 佣金率 − 广告率 − 目标净利率) = 落地成本 + 固定费用(履约费/固定手续费)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNo亚马逊品类(仅 amazon 生效),默认 home_kitchen。home_kitchen
fba_tierNoFBA 尺寸档(仅 amazon_fba 生效),默认 small_std_4_8oz。small_std_4_8oz
platformNo平台 amazon_fba / amazon_fbm / tiktok_us / shopify / aliexpress。amazon_fba
ad_cost_pctNo广告费占售价比例(%),默认 15。
landed_costYes单件落地成本(USD)。
target_margin_pctNo目标净利率(%),默认 20。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. The formula is transparent about the core calculation, but it does not explain where commission rates and fixed fees come from, how platform-specific rules affect results, or any edge cases. This is a meaningful gap for a no-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the purpose is stated first, followed by a concise formula. Every element contributes to understanding the tool, with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The formula and the fully covered schema together explain the mechanics of the calculation. However, the description omits when-to-use guidance, platform-specific fee behavior, and any caveats about default values. With no annotations and an output schema present, this is adequate but not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The formula adds meaning by linking ad_cost_pct, target_margin_pct, and landed_cost into the calculation, but it does not add detail beyond the schema for category, fba_tier, or platform. The description provides modest added value without fully compensating for any gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: reverse-calculating the breakeven price and the price needed to reach a target net margin. The formula further clarifies the resource and calculation approach. It does not explicitly name sibling tools, but the inverse-pricing purpose is distinct from cost estimation and profit estimation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus calculate_landed_cost or estimate_profit. There are no exclusions, prerequisites, or alternative conditions mentioned. The usage context is only implied by the formula and the sibling names.

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

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