Skip to main content
Glama

amazon-product-research-mcp

watchlist_list

Read-only

List the workspace's saved tracking lists (name, type, item count, whether a baseline is set). Use when the user asks 'what am I tracking', 'show my watchlists', 'my saved lists'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
list_type_inNoComma-separated list types to keep (asin/brand/seller/niche).
updated_at_toNo
max_item_countNo
min_item_countNoOnly lists with at least this many items.
updated_at_fromNoYYYY-MM-DD; only lists updated on/after this.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing a safe read operation. The description adds valuable behavioral context by specifying the returned fields (name, type, item count, baseline) and the workspace scoping, which goes beyond annotation-only information. It doesn't cover pagination or sorting, but these are minor for a simple list tool.

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 two sentences with no wasted words. It front-loads the core behavior and return fields, then supplies usage triggers. Every sentence earns its place.

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 read-only list tool with 5 optional filter parameters and no output schema, the description covers the essential return values and the main use case. It does not mention filtering behavior or parameter details, but the schema is available and the tool is relatively simple. The missing parameter descriptions in the schema slightly reduce completeness.

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

Parameters2/5

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

Schema description coverage is 60%, with two parameters (updated_at_to, max_item_count) lacking any schema description. The tool description does not compensate by explaining these or any other parameters; it only describes output fields. Since the description adds no parameter-level meaning and the coverage is not high, the agent gets limited guidance for filtering.

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 uses a specific verb ('List'), identifies the exact resource ('the workspace's saved tracking lists'), and enumerates the return fields (name, type, item count, baseline). The 'workspace's' scope distinguishes it from web-wide watchlist tools, and the list verb separates it from watchlist_add/remove/delta/stats.

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?

Provides explicit trigger phrases ('what am I tracking', 'show my watchlists', 'my saved lists') that clearly signal when to use this tool. However, it does not explicitly say when not to use it or name alternative watchlist siblings, so it falls short of a 5.

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.