Calculate Amazon FBA profit and ROI for given sell/cost prices using the full fee engine (tiered referral fees, digital services tax, VAT on fees). Call for what-if questions like 'what would I make selling at £24.99 if I buy at £12?'. Prices are in the marketplace currency.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | GB | |
| category | No | ||
| cost_price | Yes | ||
| sell_price | Yes | ||
| referral_fee_pct | No | ||
| pick_and_pack_fee | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions using the full fee engine with tiered referral fees, digital services tax, and VAT, and that prices are in marketplace currency. However, it lacks details on side effects, authentication needs, or output format, so transparency is moderate.
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 extremely concise, consisting of two sentences that front-load the key action and provide a clear example. Every sentence adds value with no waste.
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?
With 6 parameters, no output schema, and no parameter descriptions, the description is incomplete. It does not explain optional parameters or the return format, which is critical for a calculation tool. The example is helpful but insufficient for full context.
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 schema has 0% description coverage. The description adds meaning for 'sell_price' and 'cost_price' (prices in marketplace currency) but fails to explain the other four parameters (domain, category, referral_fee_pct, pick_and_pack_fee), leaving significant gaps.
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 calculates Amazon FBA profit and ROI for given sell/cost prices using a full fee engine. It uses specific verbs and resources, and is distinct from sibling tools like 'get_dashboard_summary' or 'get_product_performance'.
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 provides explicit when-to-use guidance with a what-if scenario example ('what would I make selling at £24.99 if I buy at £12?'), but does not mention when not to use or alternatives.
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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Each tool targets a distinct data type or action (e.g., product analysis, deal types, FBA operations). Even similar-sounding tools like get_deal_results and get_oa_deals are clearly separated by domain (A2A vs OA) in descriptions. No significant overlap.
All tools follow a clear verb_noun pattern with underscores (e.g., analyse_product, create_deal_task, get_credits). The consistent 'get_' prefix for retrieval tools and varied but predictable action verbs make the set easy to navigate.
At 37 tools, the set is large but covers a broad Amazon seller ecosystem (research, sourcing, FBA, deals, monitoring). Each tool serves a distinct purpose, and the count reflects the domain's complexity without being bloated.
The tool surface covers all major seller workflows: product analysis, profit calculation, sourcing, deal discovery, storefront monitoring, FBA operations, purchase tracking, price alerts, and reconciliation. No obvious gaps for core tasks.
