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Glama

AMZScout Skill + MCP

by net.amzscout.chatbot

amzscout_compare_products

Read-only

Side-by-side raw data for 2–5 Amazon products by ASIN — price, sales/revenue estimates, reviews, listing quality, plus history when available. Pure data fetch (no AI analysis) — do the comparison yourself. For a single ASIN, analyzeProduct is the equivalent. How to use: compare demand (est. sales), revenue, review moat and rating, price positioning, listing quality, and history trends (growing vs declining), then give a verdict on which product is the stronger opportunity and why.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asinsYes2–5 ASINs to compare. Each must be a real Amazon ASIN (B0XXXXXXXX). 0/O-swapped prefixes are auto-corrected.
marketplaceNoAmazon marketplace code. Default COM (United States).

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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 value by stating it's 'Pure data fetch (no AI analysis) — do the comparison yourself', clarifying the behavioral expectation that the agent must perform analysis rather than rely on tool output. This is meaningful added context beyond annotations.

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?

Well-structured with purposeful sentences: purpose, functional guardrail, sibling pointer, and usage guidance. The 'How to use' section is arguably slightly verbose but earns its place by operationalizing the comparison. Could trim a couple words but is tightly written overall.

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 comparison tool with 2 params, 100% schema coverage, and clear sibling differentiation, the description is complete. It covers what data comes back, how to use it, and warns against expecting AI analysis. No output schema exists, but the description adequately enumerates the data fields returned.

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?

Schema coverage is 100%, giving baseline 3. The description adds value by naming the exact comparison dimensions (demand, revenue, review moat, rating, price, listing quality, history trends), which guides how the asins parameter should be selected and what to do with the results. Slight extra beyond the baseline.

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?

Clear verb+resource: 'Side-by-side raw data for 2–5 Amazon products by ASIN' with specific data categories listed (price, sales/revenue, reviews, listing quality, history). Distinguishes from sibling analyzeProduct by explicitly noting the single-ASIN equivalent.

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?

Explicitly states when to use this (compare 2-5 products) and gives the alternative (analyzeProduct for single ASIN). Provides a concrete 'How to use' walkthrough of comparison dimensions. Lacks explicit 'when NOT to use' exclusion beyond the single-ASIN case, but the context is clear.

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.

Resources