MCP server for e-commerce intelligence including product data, pricing analytics, Amazon listings, and market trends for AI agents.
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one analyzes the overall store (tech stack, traffic, performance), while the other retrieves product details. There is no overlap or ambiguity.
Both tools use snake_case but the verb styles differ: 'analyze' vs 'get'. Additionally, one specifies 'shopify_store' while the other uses 'store_products', introducing minor inconsistency in naming convention.
With only two tools, the server feels incomplete for a domain as broad as e-commerce intelligence. A typical server would include 5-15 tools to cover store analysis, products, orders, customers, and analytics.
The server covers store analysis and products but misses essential e-commerce operations like order management, customer insights, inventory tracking, and detailed analytics. These gaps would force agents to rely on external tools.
analyze_shopify_storeAAnalyze a Shopify e-commerce store to extract technology stack, theme, installed apps, estimated traffic, and store performance metrics. Returns theme name, app list, tech integrations, traffic estimate, conversion data, and competitive insights. Use for competitive intelligence, market research, or e-commerce benchmarking.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Shopify store URL (e.g. 'https://www.example-store.myshopify.com' or 'example-store.com') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds value by listing output data (theme, apps, traffic, etc.) but does not disclose additional behavioral traits like data freshness or limits.
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?
Two sentences, front-loaded with purpose, then outputs and use cases. No unnecessary words.
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?
With only one parameter and no output schema, the description adequately explains what the tool returns and its use cases. It is complete for the tool's complexity.
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?
Schema coverage is 100%, so the description adds little beyond the schema's URL description. It repeats the example format, meeting the baseline.
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 verb 'analyze' and the resource 'Shopify e-commerce store', listing specific outputs (tech stack, theme, apps, etc.). It distinguishes from the sibling 'get_store_products' which focuses on product listing.
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 mentions use cases: 'competitive intelligence, market research, e-commerce benchmarking'. Does not provide negative guidance or when not to use, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_store_productsAExtract all products from a Shopify store including titles, descriptions, images, pricing, variants, and inventory status. Returns product catalog with URLs for each item. Use for competitor product research, price monitoring, or market basket analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Shopify store URL to scrape products from (e.g. 'store-name.myshopify.com') | |
| max_results | No | Maximum products to retrieve (default 50, higher values for full catalog export) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that it returns product catalog with URLs, but does not disclose additional behavioral traits like pagination, rate limits, or data freshness. Given the annotations cover the core safety profile, a score of 3 is appropriate.
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?
The description is two sentences with no wasted words. The first sentence quickly defines scope and data fields, the second provides usage context. Very efficient.
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 tool with 2 parameters and no output schema, the description covers purpose, usage, and return content well. It lists included fields, which partially compensates for missing output schema. Could be improved by noting potential limitations (e.g., only public products, pagination) but is still fairly complete.
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?
Schema coverage is 100% (both parameters described in schema). The description adds slight value for max_results by mentioning 'higher values for full catalog export', but otherwise repeats schema info. Baseline 3 is correct.
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 extracts all products from a Shopify store, listing specific data fields (titles, descriptions, images, pricing, variants, inventory status) and mentions URLs. This distinguishes it from the sibling tool analyze_shopify_store, which implies analysis rather than raw extraction.
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?
The description provides explicit use cases: competitor product research, price monitoring, market basket analysis. However, it does not directly contrast with the sibling tool or state when not to use this tool, which would improve guidance.
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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{
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"maintainers": [{ "email": "your-email@example.com" }]
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