E-Commerce Intelligence MCP Server
Server Details
MCP server for e-commerce intelligence including product data, pricing analytics, Amazon listings, and market trends for AI agents.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
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.
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 and openWorldHint, so the description adds value by listing expected outputs (theme, apps, traffic, etc.) without contradicting annotations. However, no additional behavioral details like rate limits or authentication requirements are provided.
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, front-loading the main purpose and outputs, then stating use cases. Every sentence adds value with no redundancy.
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?
Since there is no output schema, the description adequately explains return values (theme name, app list, etc.). It covers the single parameter well. Minor gap: does not mention potential limitations or data sources.
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 a description for the url parameter. The tool description does not add further meaning beyond what the schema provides, so baseline 3 is appropriate.
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 explicitly states the tool analyzes a Shopify store to extract technology stack, theme, apps, traffic, performance, and competitive insights. It clearly distinguishes from the sibling tool 'get_store_products' which focuses on 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 provides explicit use cases: competitive intelligence, market research, e-commerce benchmarking. It does not specify when not to use it, but the context of the sibling tool implies alternative for product-specific queries.
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 already indicate readOnlyHint and openWorldHint. Description adds that tool returns product catalog with URLs, but does not reveal behavior beyond what annotations imply (e.g., rate limits, pagination). No contradiction.
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 with no filler. First sentence states purpose and output, second lists use cases. Front-loaded and 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?
Despite no output schema, description enumerates typical output fields (titles, pricing, variants, etc.) and indicates product catalog with URLs. Sufficient for agent to understand what is returned.
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 100%: both parameters fully described. Description restates 'Shopify store URL' and hints at max_results, but adds minimal new semantics beyond schema.
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?
Description clearly states verb 'Extract' and resource 'all products from a Shopify store', listing specific attributes (titles, pricing, variants, etc.). Distinguishes from sibling 'analyze_shopify_store' by focusing on product extraction rather than analysis.
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?
Provides explicit use cases: competitor product research, price monitoring, market basket analysis. Does not mention when not to use or contrast with siblings, 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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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Credentials required to access the server are missing or invalid
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