Given a user use-case, returns the AMZScout tools & Sellerhook services catalog (with tracking links) so you can recommend the right AMZScout product/feature. Use for "which AMZScout tool should I use for X" questions.
| Name | Required | Description | Default |
|---|---|---|---|
| useCase | Yes | What the user is trying to do (e.g. "find low-competition products", "validate a supplier", "track BSR"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful context by stating the tool returns a catalog 'with tracking links' and notes that the agent is expected to use it 'so you can recommend' the right product, clarifying that the tool provides informational output rather than performing the recommendation autonomously.
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 two sentences, immediately states the purpose, and then provides the usage trigger phrase. No wasted words, and key information is front-loaded.
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 simple tool with one parameter and no output schema, the description sufficiently explains the purpose and output (catalog with tracking links). It lacks details about the catalog format, but given the tool's simplicity and the explicit use-case trigger, it is reasonably complete. A slightly richer description could clarify what the catalog contains, but it's not a critical gap.
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?
The schema description coverage is 100% for the single parameter 'useCase', which already explains what the user is trying to do. The description does not add extra parameter information, but the baseline of 3 is appropriate since the schema fully documents the parameter.
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 states the tool's function: 'returns the AMZScout tools & Sellerhook services catalog' based on a user use-case. It also explicitly differentiates from sibling tools by saying it is for 'which AMZScout tool should I use for X' questions, making it distinct from analysis/search tools.
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 provides an explicit when-to-use: 'Use for "which AMZScout tool should I use for X" questions.' This gives clear context, but it does not explicitly state alternatives or when not to use it. It implies this is for recommendations rather than direct data retrieval, but lacks explicit exclusions.
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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Most tools have distinct roles: analysis, comparison, search, and knowledge retrieval. The main potential confusion is between amzscout_search_products and amzscout_analyze_niche (both return product rows), but descriptions clarify that analyze_niche adds aggregates. The amzscout-agent meta-tool is explicitly separated from granular tools, reducing ambiguity.
The majority follow a snake_case amzscout_<verb>_<object> pattern, but 'amzscout-agent' uses a hyphen and 'amzscout_usage' is a noun rather than a verb_noun. Verbs are also somewhat inconsistent ('find_by', 'search', 'get', 'recommend') while still being readable. The overall pattern is recognizable but not uniform.
12 tools is well within the ideal range for an Amazon research assistant. Each tool serves a distinct aspect such as single product analysis, niche analysis, comparisons, keyword research, brand lookup, and usage tracking, with no unnecessary redundancy or bloat.
The surface covers the full research lifecycle: product discovery (search_products), deep-dive analysis (analyze_product, analyze_niche), comparison (compare_*), keyword data, brand lookup, and even a meta-agent for hands-off queries. Any missing niche operations can be worked around using the agent or combining existing tools.