Skip to main content
Glama

amazon-product-research-mcp

set_cost_floor

Destructive

Record the user's cost basis (landed unit cost) for an ASIN so later checks can flag when the buy box drops below their cost and compute true net spread; the saved/cleared confirmation also echoes the ASIN's product brand, title and price (or price range) and its fulfillment (FBA/FBM/AMZ). Use when the user says 'my cost on B0... is $12', 'set my cost basis', 'I pay $X for this'. One ASIN per call; pass cost 0 to clear.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinYesThe 10-character ASIN.
cost_floorYesYour landed unit cost (marketplace currency). 0 clears it.
marketplace_idNoMarketplace (2 = Amazon US).

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already mark it as destructive and not read-only; the description adds the important clearing behavior (cost 0 clears) and discloses the confirmation payload (brand, title, price/price range, fulfillment). This goes beyond the structured annotations and gives useful behavioral context.

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

Conciseness4/5

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

The description is dense but efficient: purpose first, then trigger phrases, then the clear-by-zero edge case. It earns its length by covering behavior and usage that are not present in the schema, though the first sentence is a bit long.

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

Completeness4/5

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

For a simple setter with no output schema, the description covers why the tool exists, how to invoke it, what the confirmation echoes, and how to clear a value. Minor missing context like overwriting an existing value is not a serious gap given the destructive annotation.

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 schema already documents asin, cost_floor, and marketplace_id. The description reinforces that cost_floor is a landed unit cost and that 0 clears it, but adds little parameter-level meaning beyond the schema.

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 verb ('Record'), a specific resource ('the user's cost basis for an ASIN'), and a concrete downstream purpose ('later checks can flag when the buy box drops below their cost'). This clearly separates it from the analytic and watchlist sibling tools, which do not write cost-basis data.

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?

It gives explicit trigger phrases ('my cost on B0... is $12', 'set my cost basis', 'I pay $X for this') and a clear one-ASIN-per-call rule. It does not explicitly state when not to use the tool or name alternatives, but no close alternative exists among the siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.