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amzscout_demo

Read-only

Start AMZScout: returns a welcome message and the example questions the user can try. Call this at the very START of the conversation, and whenever the user greets you or says things like "Ask AMZScout" (case-insensitive — "ask amzscout", "ASK AMZSCOUT" and any other capitalization all count), "demo", "begin", "hello", "help", or "what can you do". Relay its output verbatim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior4/5

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

The description states it returns a welcome message and example questions, and instructs to relay output verbatim. While the readOnly annotation is present, the description adds context about the output and handling, which is useful for the agent.

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 concise enough, though it repeats the trigger examples. It is not overly verbose and is structured in a single clear sentence, which is acceptable.

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

Completeness5/5

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

Given the simple nature (no params, no output schema), the description fully covers what the tool does, when to use it, and how to handle its output. No additional information is needed.

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

Parameters4/5

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

The tool has zero parameters, so the baseline is 4. The description does not need to explain any parameter details, and the empty schema is fully covered.

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?

Clearly states it returns a welcome message and example questions, and explicitly distinguishes itself from other AMZScout tools by its demo/greeting purpose. The name 'amzscout_demo' is reinforced by the description, making its function unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage triggers: call at the very start of conversation, on greetings, or when user says specific phrases like 'Ask AMZScout', 'demo', 'begin', etc. This leaves no ambiguity about when to invoke the tool.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: analyze (single niche/product/set), compare (niches/products), search (products/keywords/brand/knowledge), and auxiliary (demo, usage, recommend). The high-level amzscout-agent is explicitly positioned as an alternative to granular tools, reducing ambiguity even with its broader scope.

Naming Consistency4/5

Most tools follow a consistent amzscout_verb_noun pattern (e.g., amzscout_analyze_niche, amzscout_compare_products, amzscout_get_keywords). The only deviation is amzscout-agent, which uses a hyphen instead of an underscore, breaking the uniform naming style.

Tool Count5/5

13 tools is well within the ideal 3-15 range and each earns its place by covering analysis, comparison, search, keywords, brand lookup, knowledge retrieval, usage, and a demo entry point. The count feels justified for the scope of an Amazon research assistant.

Completeness5/5

The tool surface covers the full lifecycle of Amazon research: single and multi-product analysis, niche analysis and comparison, keyword/SEO data, brand footprint, product search, knowledge grounding, token usage, and a recommendation helper. No obvious gaps for the domain, as even history-based trends are included via analysis tools.

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