Current sellers and their stock levels for an Amazon ASIN. Call when the user asks who is selling a product, how many sellers there are, or how much stock competitors hold. Costs 1 analyse credit unless the ASIN was analysed in the last 24 hours.
| Name | Required | Description | Default |
|---|---|---|---|
| asin | Yes | ||
| domain | No | GB |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses credit cost and caching condition (24-hour credit exemption), but no annotations exist. Does not mention rate limits, authentication, or error handling, which are important for a tool with no 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?
Two sentences, front-loaded with purpose and usage, no superfluous words. Ideal conciseness.
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 read tool, covering purpose, usage, and cost. However, without parameter descriptions or output schema, the agent may not fully understand return format or parameter constraints.
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%, and description only mentions 'Amazon ASIN' but fails to explain the 'domain' parameter, which determines the marketplace. Agent may not know to specify a non-default domain.
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 current sellers and stock levels for an ASIN. It is specific but does not explicitly differentiate from siblings like get_product_performance, though the function is distinct.
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?
Explicitly lists three use cases (who is selling, number of sellers, competitor stock) and mentions credit cost with condition. Lacks explicit when-not-to-use or alternatives.
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.
