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amazon-product-research-mcp

authorized_seller_set

Destructive

Save the authorized sellers for a brand you own/manage — the whitelist that makes 'unauthorized seller' detection possible. The brand must already be tracked in this workspace. Use when the user says 'these are my authorized sellers for X', 'add Y to my authorized list', 'set my brand's approved resellers'. Pass the seller names exactly as they appear on Amazon.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandYesThe brand (must be a tracked workspace brand).
sellersYesAuthorized seller names to save.
marketplace_idNo1 = Amazon UK, 2 = Amazon US (default), 4 = Amazon CA, 5 = Amazon AU, 6 = Amazon DE, 7 = Amazon JP, 8 = Amazon IT, 9 = Amazon FR, 10 = Amazon ES, 11 = Amazon MX, 12 = Amazon BR

TDQS

A3.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations mark this as destructive (destructiveHint=true), and the description does not contradict that. However, the description never discloses whether calling the tool replaces the entire existing whitelist or merges with it. Given the 'add Y' example, this ambiguity could lead an agent to pass only the new seller and accidentally wipe the authorized list.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the core purpose, and every sentence earns its place: purpose, precondition, trigger examples, and exact-name guidance. No filler or redundant restatement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, preconditions, trigger phrasing, and exact seller-name handling. However, it omits the overwrite-versus-merge behavior, which is a critical piece of context for a destructive set operation and for correctly handling 'add Y' requests.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds one useful operational detail—seller names must be passed exactly as they appear on Amazon—but provides little else beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Save') with a clear resource ('the authorized sellers for a brand') and explains the business purpose: the whitelist that makes unauthorized-seller detection possible. It also distinguishes naturally from the sibling authorized_seller_list by framing this as 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.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit trigger phrases ('these are my authorized sellers for X', 'add Y to my authorized list') and states a precondition: the brand must already be tracked in the workspace. It does not explicitly name the alternative read tool, but the save-vs-list distinction is strongly implied.

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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TDQS

A3.5/5.0
Disambiguation2/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness5/5

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.