get_fba_healthAmazon FBA inventory health: aging buckets (0-90 … 365+ days), long-term storage-fee exposure, and recommended actions (e.g. remove/restock).
| Name | Required | Description | Default |
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No arguments | |||
| Name | Required | Description | Default |
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No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations by detailing the output contents (aging buckets, fee exposure, recommended actions), which helps the agent understand what the tool computes without contradicting the 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, tightly packed sentence that conveys the tool's scope, key outputs, and examples without any filler. It is front-loaded with the essential purpose ('Amazon FBA inventory health') and immediately provides concrete details.
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 no parameters and an output schema present, the description only needs to convey what the tool covers and returns. It does so comprehensively: aging buckets, fee exposure, and recommended actions. The presence of an output schema means return format details need not be in the description, so this is complete for invocation and selection.
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 tool has zero parameters, so schema coverage is trivially 100% and the description cannot add parameter meaning. Per rubric, 0 params earns a baseline of 4. The description focuses on the output semantics rather than parameters, which is appropriate.
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 FBA inventory health with specific details: aging buckets (0-90 to 365+ days), long-term storage-fee exposure, and recommended actions. This specific verb+resource pairing distinguishes it from sibling tools like get_inventory or get_stock_status, which focus on raw inventory levels rather than health and recommendations.
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 no guidance on when to use this tool versus alternatives. It does not mention exclusions or reference sibling tools such as get_inventory, get_analytics_summary, or get_dashboard_summary, leaving the agent to infer appropriateness solely from the tool's name and title.
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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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.