Add a product to the user's sourcing list (their buy list). Call when the user says 'add this to my buy list' — typically after analysing a product, passing through the numbers from the analysis. sourced_from is where they'd buy it (retailer/site).
| Name | Required | Description | Default |
|---|---|---|---|
| roi | No | ||
| asin | No | ||
| cost | No | ||
| notes | No | ||
| title | Yes | ||
| company | No | ||
| sales_rank | No | ||
| sourced_from | No | ||
| monthly_sales | No | ||
| projected_profit | No | ||
| projected_sale_price | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains the action and clarifies one parameter but lacks depth on side effects, permissions, or return behavior.
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, no wasted words, front-loads the primary purpose and usage trigger.
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 add-to-list tool, but lacks details on return values, success/failure behavior, and how the list is affected. Could be more 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?
Schema coverage is 0%; description only mentions 'sourced_from' and implies data comes from analysis. For 11 parameters, this is insufficient to fully understand each field's purpose.
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?
Clearly states the action (add), the resource (product to sourcing list), and provides typical usage scenario after analysis. Distinguishes from sibling tools by being a write operation.
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 says to call when user says 'add this to my buy list' and typically after analysis. No exclusion or alternative tools mentioned, but 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.
