Per-SKU profit & loss for one calendar month: revenue, Amazon fees, COGS, refunds and net profit, plus account totals. Call for 'what was my profit in ?' or 'break down last month's P&L'. month is 'YYYY-MM'. Requires a connected Amazon account.
| Name | Required | Description | Default |
|---|---|---|---|
| month | Yes | ||
| vat_registered | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions the requirement of a connected Amazon account and lists data returned. However, it does not disclose potential limitations like caching, rate limits, or whether all SKUs are included.
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?
Two focused sentences plus a format note. Informative without redundancy, though the format note could be integrated more smoothly.
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?
Adequate for a simple tool with two parameters. Covers main purpose and usage, but missing details on behavioral aspects and the second parameter. No output schema increases need for completeness.
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 0%, so description must compensate. It specifies the month format 'YYYY-MM' but does not mention the vat_registered parameter at all, leaving its purpose and usage unclear.
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?
Description clearly states the tool returns per-SKU profit & loss for a calendar month, listing specific metrics like revenue, fees, COGS, etc. It includes example queries and distinguishes from siblings by focusing on monthly financial reporting.
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?
Provides explicit when-to-use triggers ('what was my profit in <month>?') and format for the month parameter. Lacks explicit alternatives or when-not-to-use guidance, but the context and sibling list make the use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
