Check the status of a live Amazon refresh kicked off for an ASIN (returns its current snapshot when ready). job_id is the ASIN. Use after a tool says a live refresh is collecting.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job id (the ASIN being refreshed). | |
| marketplace_id | No | Marketplace (2 = Amazon US). Default 2. |
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 that the tool returns a snapshot when ready, but it does not disclose what happens if the refresh is not ready (e.g., whether it blocks, returns an interim status, or errors).
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 carry purpose, behavior, parameter clarification, and usage timing with no filler. The most critical decision-relevant detail—when to call it—is placed at the end but clearly stated.
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 low-complexity, read-only status tool with fully documented parameters, the description is nearly complete. The only notable gap is the lack of detail about the 'not ready' case, which an agent might need to know for polling or retry behavior.
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 description coverage is 100%, so the input schema already documents job_id and marketplace_id. The description's 'job_id is the ASIN' simply restates the schema text and adds no new semantic information beyond it.
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 uses a specific verb ('Check the status') and a clear resource ('a live Amazon refresh kicked off for an ASIN'), and explicitly states that the result is 'its current snapshot when ready.' This distinguishes it from initiation tools like collect_asin_now and from data-analysis tools among the siblings.
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 gives a clear trigger condition: 'Use after a tool says a live refresh is collecting.' This tells the agent when the tool is appropriate, though it does not name the specific sibling alternative or state explicit exclusions (e.g., not for historical refreshes).
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.
The tool set is extremely granular, with multiple clusters that overlap in purpose (e.g., amazon_search_results/search_products/shopping_search; watchlist_delta/watchlist_diff; find_undercompeted_brands/category_undercompeted_brands; operator_new_brands/operator_new_on_brand). Although descriptions are detailed, the boundaries between many 'find opportunity' and 'watchlist change' tools are subtle enough that an agent could easily misselect.
The vast majority follow a verb_noun snake_case convention with clear prefixes (asin_, brand_, category_, operator_, watchlist_, playbook_, find_, top_). A few noun-style exceptions (competitive_landscape, risk_assessment, brand_under_attack, buybox_loss_alert) break the pattern, but they are minor and do not obscure the overall scheme.
With 82 tools, the server is far beyond the 50+ extreme threshold. Even though the domain is broad, many tools are highly granular variants (e.g., filter_brands_by_fba_share vs filter_operators_by_fba_share; watchlist_delta vs watchlist_diff) that could be merged or parameterized, imposing a heavy cognitive and context burden on agents.
The surface is extraordinarily complete for Amazon product research: discovery, ASIN/brand/category analytics, buybox and BSR history, sourcing evaluation, risk/MAP monitoring, watchlists, playbooks, operator intelligence, cross-marketplace checks, and live refreshes. Workflows like authorized_seller_set → buybox_loss_alert and watchlist_add → watchlist_delta are fully supported, with no obvious dead ends.
