Reimbursements Amazon has paid the user (lost/damaged stock etc). Call when the user asks what Amazon has paid them back. Requires a connected Amazon account (closed beta).
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| limit | No | ||
| reason | No | ||
| search | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully convey behavioral traits. It correctly indicates the tool is a read operation for past reimbursements and mentions the closed beta requirement. However, it lacks details on pagination behavior, data recency, or any side effects, leaving gaps for the agent.
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 long, front-loading the purpose and then providing usage context and a requirement. Every sentence serves a clear purpose with no redundancy or wasted words.
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 4 parameters with 0% schema coverage and no output schema, the description lacks essential details. It does not explain how to use parameters or what the return data looks like, making it incomplete for effective use despite clear overall purpose.
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 should compensate by explaining parameters. It does not mention page, limit, reason, or search at all. The agent must infer their meaning from names alone, which is insufficient for correct invocation.
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 returns reimbursements paid to the user for lost/damaged stock, which is a specific and distinct purpose. However, it does not explicitly differentiate from sibling tools like get_fba_refunds or get_fba_returns, missing an opportunity to clarify when to use this tool versus alternatives.
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 a clear usage cue: 'Call when the user asks what Amazon has paid them back' and notes the prerequisite of a connected Amazon account. However, it does not specify when NOT to use this tool or offer alternatives, such as using get_fba_refunds for refunds instead.
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.
