Start a Wholesale Scanner run against a supplier price list. Each item in
products is a dict with 'title', 'price' (cost per unit), and 'ean'
(barcode — needed to match, the default scan is EAN-based). Up to 500 items.
Matches each product to Amazon and returns profit/ROI. Returns a job_id —
poll get_wholesale_status(job_id) for results. Premium feature.
| Name | Required | Description | Default |
|---|---|---|---|
| products | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the process (matches to Amazon), return value (job_id), limit (500 items), and premium nature. It does not cover error handling or rate limits, but core behavior is well-described.
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 concise with five sentences, no fluff, and key information is front-loaded (purpose, then details, then next step).
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?
For a tool with no output schema and one parameter, the description explains the parameter structure, behavior, return value, and next step. It could add more about error handling or prerequisites, but is reasonably complete.
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 no parameter description (coverage 0%), but the description fully explains the expected structure of each product dict (title, price, ean) and the maximum number of items. This is essential for correct usage.
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 starts a Wholesale Scanner run against a supplier price list, matches products to Amazon, returns profit/ROI, and returns a job_id. This distinguishes it from sibling tools like get_wholesale_status which polls results.
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 explains when to use this tool (to start a scan), includes the format and limit of products, and mentions polling with get_wholesale_status. It could explicitly state when not to use, but the context is 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.
