ecommerce-intelligence-mcp-server
Server Details
Shopify store + product analysis for DTC competitive research.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.1/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one analyzes store technology and performance, the other extracts product catalog data. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern (analyze_shopify_store, get_store_products) with clear prefixes describing the action.
With only 2 tools, the server feels severely under-scoped for e-commerce intelligence. A proper set would include order, customer, analytics, and marketing tools, making this count too low.
The domain implies comprehensive e-commerce intelligence, but only store analysis and product listing are covered. Critical operations like order insights, customer data, or competitor tracking are missing, causing major gaps.
Available Tools
2 toolsanalyze_shopify_storeARead-onlyInspect
Analyze 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') |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, which the description does not contradict. The description adds behavioral context by listing the types of data returned (theme, apps, traffic, etc.) but does not disclose potential rate limits, external API dependencies, or authentication requirements beyond what is implied.
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 three sentences: action and outputs, specific outputs, and use cases. Front-loaded with the primary action, no wasted words, all sentences earn their place.
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?
Given the simple schema (1 parameter, 100% coverage) and no output schema, the description adequately explains inputs and outputs. It covers purpose, outputs, and use cases. Could be slightly more complete by mentioning the output format, but overall sufficient.
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%, with the url parameter fully described. The description adds the example formats but does not provide additional semantics beyond what the schema already states. A baseline score of 3 is appropriate since the schema carries the burden.
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 analyzes a Shopify store to extract technology stack, theme, apps, traffic, and performance metrics, using specific verbs and listing concrete outputs. It distinguishes from the sibling tool 'get_store_products' by focusing on store-level analysis rather than product data.
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 explicitly states 'Use for competitive intelligence, market research, or e-commerce benchmarking', providing clear usage context. However, it does not mention when not to use this tool or suggest alternatives, though the single-parameter design makes overuse unlikely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_store_productsARead-onlyInspect
Extract 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) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint) already declare non-destructive, open-world behavior. Description adds that it returns a catalog with URLs, but lacks details on rate limits, authentication, or result structure beyond what's implied.
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 efficiently cover purpose, return value, and use cases without waste. No repetition or filler.
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?
Despite lacking an output schema, the description adequately explains the tool's return (product catalog with URLs) and intended use. For a simple 2-parameter read tool, it is sufficiently 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% with clear parameter descriptions (url, max_results with default). Description reinforces the product data scope but does not add new meaning beyond schema for these parameters.
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 'Extract all products from a Shopify store' with specific data types (titles, descriptions, images, pricing, variants, inventory) and distinguishes from the sibling tool 'analyze_shopify_store' which suggests analysis versus 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?
Explicitly mentions use cases ('competitor product research, price monitoring, or market basket analysis'), but does not provide when-not-to-use guidance or contrast with alternatives beyond the sibling name.
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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