TF-IDF search across the AMZScout knowledge base (Amazon-seller tutorials, brand reference, glossary). Returns the top-K relevant chunks with title, source URL and text. Use this to ground answers in factual material.
| Name | Required | Description | Default |
|---|---|---|---|
| topK | No | How many knowledge chunks to return (1–20) | |
| query | Yes | Search phrase. 2-300 chars. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation indicates readOnlyHint=true, lowering the burden. The description adds meaningful behavioral context: it reveals the search algorithm (TF-IDF), the content scope, and the return structure (top-K chunks with title, URL, text). This goes beyond simply stating the tool is a safe read.
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 three concise sentences: purpose, return format, and usage. It is front-loaded with the verb and resource, no filler, and every clause contributes value.
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 read-only search tool with two well-documented parameters and no output schema, the description fully covers what the tool does and returns. It is complete and needs no further explanation.
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?
Schema description coverage is 100% for both parameters (query and topK), so per rubric the baseline is 3. The description does not add significant parameter-level meaning beyond what the schema already provides, though it does contextualize 'top-K' and 'search'.
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 specifies a distinct verb ('search') and resource ('AMZScout knowledge base') with content types (tutorials, brand reference, glossary). It clearly identifies the return payload (chunks with title, source URL, text), which distinguishes it from sibling analysis tool.
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 clear context for when to use the tool ('Use this to ground answers in factual material') but does not explicitly name alternatives or state when not to use it. This is a clear usage context without exclusions.
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.
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.