Remove items from a saved tracking list, or delete the whole list. Provide list_type + name; give items to drop just those, or omit items to delete the entire list. Use when the user says 'stop tracking ...', 'remove ... from my watchlist', 'delete my ... list'.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| items | No | Items to remove; omit to delete the list. | |
| list_type | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already mark this as destructive, and the description adds meaningful specifics: omitting items deletes the entire list, while providing items removes only those. This clarifies the destructive scope beyond the annotation alone. No contradiction exists between the description and the destructiveHint annotation.
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 compact and front-loaded with the core behavior. There is slight redundancy between 'delete the whole list' and 'omit items to delete the entire list', but the second sentence earns its place by mapping parameters to behavior.
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?
The description covers both supported operations and the parameter constraints needed to execute them. Since there is no output schema, the lack of return-value detail is acceptable; the destructive nature is already in annotations, and the description adds the specific deletion semantics.
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?
With only 33% schema description coverage, the description compensates by clarifying that list_type and name identify the list, and by explaining the crucial optional behavior of items — provide items to remove specific ones, omit them to delete the list. It could more explicitly define 'name' as the list name, but the intended meaning is inferable from context.
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 identifies the operation as removing items from a saved tracking list or deleting the whole list. It uses specific verbs and resources, and the 'delete the entire list' option distinguishes it from simple item removal and from sibling tools like watchlist_add or watchlist_list.
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 explicit natural-language triggers such as 'stop tracking ...', 'remove ... from my watchlist', and 'delete my ... list', which directly tell an agent when to invoke this tool. It does not explicitly contrast it with sibling tools like watchlist_add, but the use-case phrasing is clear enough to route correctly.
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.
