ecommerce-fashion-market-analysis
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GSC_SITE_URL | No | Search Console property URL | |
| SHOPIFY_STORE | No | Shopify store domain | |
| GSC_CLIENT_EMAIL | No | Search Console service account email | |
| GSC_PRIVATE_KEY_PATH | No | Path to JSON private key | |
| SHOPIFY_ACCESS_TOKEN | No | Admin API access token |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| product_seo_auditA | Audit a fashion product page for SEO best practices. Checks meta title length, meta description, product structured data (JSON-LD), image alt text, URL structure, H1 presence, fashion-specific keywords (size, fit, material), and seasonal context. Returns a score (0-100), per-check results, and actionable recommendations. Use this whenever a fashion brand wants to optimize a product listing for organic search. Works for any e-commerce platform (Shopify, Magento, custom). Cost control: use verbose (0=quick check ~50 words, 1=standard ~200 words, 2=detailed ~500 words) and max_words to control output size and token cost. |
| fashion_trend_analysisA | 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. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools serve entirely different purposes: one analyzes fashion trends, the other audits product SEO. There is no overlap or ambiguity.
Both tools use consistent snake_case naming with a clear pattern: fashion_trend_analysis and product_seo_audit. The naming is descriptive and predictable.
With only 2 tools, the server is undersized for its stated domain of e-commerce fashion market analysis. A typical server of this scope would need at least 5-10 tools to cover core functionalities like product data, competitor analysis, etc.
The server lacks essential tools for market analysis, such as product retrieval, competitor benchmarking, pricing data, or inventory insights. The trend analysis is limited to a few categories, and the SEO audit is a single-point check. Significant gaps prevent comprehensive market analysis.