Detect ecommerce platforms (Shopify, WooCommerce, Magento, PrestaShop, Wix, BigCommerce, OpenCart, Squarespace) for any online store via API
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Usage analytics
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Average 3.5/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion or overlap between tools. The tool has a clear, singular purpose.
The single tool name 'detect_platform' follows a clear verb_noun convention, and there are no other tools to create inconsistency.
The server has exactly one tool, which feels thin for a general-purpose server but may be appropriate if the scope is extremely narrow. It falls in the borderline category.
The tool fully covers the stated domain of detecting ecommerce platforms. There are no missing operations for this narrow purpose.
detect_platformAInspectDetects the ecommerce platform of any online store.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Domain or URL of the store |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, any side effects, rate limits, or error handling. The verb 'detects' hints at a safe operation but does not confirm it, leaving significant ambiguity for an agent.
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 a single sentence that is direct and free of filler. Every word adds value, making it highly 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 one parameter and no output schema, the description is minimal but lacks details about the return value, error conditions, or edge cases (e.g., invalid URLs). It is adequate for basic understanding but not fully 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?
The input schema provides 100% coverage with a clear description of the 'url' parameter. The tool description adds no additional meaning beyond the schema, so the baseline score of 3 applies.
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's function with a specific verb ('detects') and resource ('ecommerce platform of any online store'). It is fully self-contained and unambiguous, even without sibling tools for differentiation.
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 implies usage for identifying the ecommerce platform of online stores, but provides no explicit guidance on when to use it versus alternatives, nor any prerequisites or exclusions. Since no sibling tools exist, this is acceptable but not exemplary.
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
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
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
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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