Create an A2A deal-monitor task: the user gets notified whenever the deal feed finds products matching these filters. Call when the user says e.g. 'notify me about deals over £5 profit and 40% ROI in Toys'.
| Name | Required | Description | Default |
|---|---|---|---|
| source | No | ||
| min_roi | No | ||
| category | No | ||
| task_name | Yes | ||
| min_profit | No | ||
| keepa_drops | No | ||
| max_asin_rank | No | ||
| min_monthly_sales | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It discloses that the tool creates a notification task and that the user gets notified on matching deals. It does not mention persistence, limits, or whether it is a write operation, but the core behavior is clear.
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, first stating the purpose and second giving a usage example. No wasted words, front-loaded, and easy to parse.
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?
Given 8 parameters, no schema descriptions, no output schema, and no annotations, the description is insufficient. It lacks explanations for most parameters, does not mention return values, error cases, or operational constraints. The example provides some context but is not comprehensive.
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 input schema has 8 parameters with 0% description coverage. The description only hints at min_profit, min_roi, and category via the example, leaving source, keepa_drops, max_asin_rank, and min_monthly_sales unexplained. The description does not compensate adequately for the lack of schema descriptions.
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 it creates an A2A deal-monitor task that notifies the user on matching filters. It distinguishes from siblings like get_deal_tasks and get_deal_results by specifying the creation action. The example invocation reinforces the purpose.
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 a concrete usage scenario with an example user request. It explicitly says 'Call when the user says...'. However, it does not cover when not to use or compare to alternatives like get_deal_tasks for listing existing tasks.
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.
