The caller's AMZScout AI-agents token balance — remaining, used, and limit. Free — no tokens are charged for this call. How to use: answer "how many tokens do I have left", "what's my usage / balance / limit", or when a call fails on quota. Report the remaining figure first.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description adds key behavioral context: the call is free and does not charge tokens. It also instructs to report the remaining figure first, which sets response expectations. No side effects exist due to read-only nature, so this is sufficient.
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?
Three concise sentences each serve a purpose: resource definition, cost disclosure, and usage guidance. Front-loaded with the core purpose, no redundant words, and tightly structured for quick comprehension.
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 zero-parameter read-only tool, the description fully covers purpose, usage triggers, cost implications, and response emphasis. It is complete enough for an agent to invoke correctly without an output schema.
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 tool has zero parameters, so schema coverage is trivially 100%. The description compensates by clarifying the output semantics (remaining, used, limit) and the free-of-charge behavior, which adds value beyond the empty schema.
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 reports the caller's AMZScout AI-agents token balance (remaining, used, and limit). It uses a specific noun phrase ('token balance') and differentiates from sibling analysis tools by focusing on usage metadata, not product/niche insights.
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?
Explicitly provides when to use: answer questions like 'how many tokens do I have left' or 'usage/balance/limit', and also when a call fails on quota. It clearly implies this is the go-to tool for token accounting, contrasting with the analytical siblings.
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.