Skip to main content
Glama
alexgenovese

ecommerce-fashion-market-analysis

fashion_trend_analysis

Read-onlyIdempotent

Analyze fashion trends for denim, sneakers, bags, dresses, or custom categories to identify trending keywords, colors, silhouettes, and price tier demand for content planning and merchandising strategy.

Instructions

Analyze current fashion trends for a specific product category. Returns trending keywords (search volume direction), trending colors with hex codes, silhouette trends (rising/peaking/declining), price tier demand, and key strategic insights. Built-in data for: denim, sneakers, bags, dresses. Other categories return limited data with an invitation for custom research.

Use this when a fashion brand needs trend intelligence for content planning, product development, or seasonal merchandising strategy.

Cost control: use verbose (0=quick insight ~50 words, 1=standard ~200 words, 2=detailed ~500 words) and max_words to control output size and token cost.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoTarget market (default: global)
seasonNoSeason filter (default: current detected season)
verboseNoOutput detail level: 0=minimal (top keywords + insights, ~50 words), 1=standard (~200 words), 2=detailed (~500 words). Default: 1.
categoryYesProduct category (denim, sneakers, bags, dresses, or custom)
max_wordsNoMaximum words in response. Controls token cost. Default: 200. Overrides verbose budget if set.
timeframeNoAnalysis timeframe (default: 1m)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketYes
seasonYes
categoryYes
generatedAtYes
keyInsightsYes
priceRangesYes
silhouettesYes
trendingColorsYes
trendingKeywordsYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral details: returns specific output types, notes built-in data limitations for certain categories, and cost control via verbose/max_words. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise at ~150 words, front-loaded with purpose, then details. Every sentence adds value. Well-structured with clear sections.

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?

With output schema present, description needn't detail return values. Covers main features: supported categories, output types, cost control, and usage context. Slightly lacking on how timeframe affects data, but adequate.

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%, baseline 3. Description adds value by explaining verbose levels (quick insight, standard, detailed) and cost control. Also mentions default season detection and category limitations. Contributes beyond schema.

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?

Description clearly states 'Analyze current fashion trends for a specific product category' and lists specific outputs (keywords, colors, silhouettes, etc.). It mentions supported categories and distinguishes from sibling 'product_seo_audit' implicitly by focusing on trends.

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?

Explicit usage context: 'Use this when a fashion brand needs trend intelligence for content planning, product development, or seasonal merchandising strategy.' Also provides cost control guidance. No explicit when-not-to-use or alternatives, but clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/alexgenovese/ecommerce-fashion-market-analysis'

If you have feedback or need assistance with the MCP directory API, please join our Discord server