get_reimbursements_summaryAmazon FBA reimbursements over a window: total amount (major units), count, units, and the top reimbursement reasons.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days (default 30). |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds context about the window and output metrics, but does not disclose any additional behavioral traits like data latency, currency units, or aggregation specifics. It does not contradict annotations.
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, well-structured sentence that front-loads the key information (Amazon FBA reimbursements) and lists the output components without unnecessary words. It is highly concise and scannable.
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 simple with one optional parameter, an output schema exists, and annotations cover safety. The description adequately covers the purpose, scope, and key outputs, making it complete for an agent to select and invoke the tool 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 description coverage is 100% and the sole parameter 'days' is well-documented in the schema. The tool description adds little beyond the word 'window' which aligns with the schema, so it does not provide significant additional meaning.
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's function: retrieving an Amazon FBA reimbursements summary. It specifies the key metrics (total amount, count, units, top reasons) and the resource (reimbursements), distinguishing it from sibling tools like get_payments_summary or get_returns_summary.
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 usage context (need a reimbursement summary over a window) but does not explicitly mention when to prefer this tool over alternatives or when not to use it. There are many similar summary tools among siblings, so more explicit guidance would be helpful.
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.
Most tools target distinct data points (e.g., get_analytics_summary vs get_brand_score). However, 'search' and 'search_catalog' have overlapping functionality and could cause confusion, and 'fetch' is a helper tied to 'search', adding minor ambiguity.
The majority use consistent verb_noun snake_case (e.g., get_inventory, list_stores). Exceptions like 'fetch' and 'search' (without object) break the pattern, but they are few.
43 tools is excessive for a data-retrieval-only API. Many get_* and search_* tools could be consolidated (e.g., search_catalog, search_inventory, search_orders are similar). The large number will overwhelm an agent.
The tool set is entirely read-only (get, search, list, fetch). There are no create, update, or delete tools, which is a critical gap for managing e-commerce operations. Agents can only view data, not act on it.