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Hosted MCP for e-commerce: live product catalog, stock, and pricing for AI agents.
- Status
- Healthy
- Last Tested
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- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 4.1/5 across 5 of 5 tools scored. Lowest: 3.3/5.
Tools are mostly distinct, but check_stock and get_price overlap significantly as both return price. Descriptions clarify check_stock includes inventory and shipping, but an agent might mistakenly use get_price when check_stock would also provide the price. get_product_details also overlaps with check_stock's product info. However, clear descriptions reduce ambiguity.
All tool names follow a consistent verb_noun pattern with snake_case: check_stock, get_price, get_product_details, list_products, search_products. No deviations.
5 tools is well-scoped for a product inventory server. Each tool covers a distinct operation: check stock, get price, get details, list all, search. Not too few nor too many.
The set provides read operations (list, search, details, price, stock) but lacks any write operations like create, update, or delete products. This makes it incomplete for full product management, though it may suffice for a read-only use case.
Available Tools
5 toolscheck_stockAInspect
Check real-time inventory, price, and shipping for a product SKU.
This tool queries the connected e-commerce platform (Shopify, WooCommerce, etc.)
for live inventory data. Returns current stock level, price, and
availability status.
Args:
sku: Product SKU (Stock Keeping Unit) - e.g., "RED-WIDGET-001"
Returns:
Dictionary with:
- sku: The requested SKU
- stock: Current inventory count
- price: Current price in USD
- can_ship_today: Boolean indicating same-day shipping availability
- message: Human-readable status message
Example:
>>> await check_stock("WIDGET-001")
{
"sku": "WIDGET-001",
"stock": 42,
"price": 29.99,
"can_ship_today": True,
"message": "✅ WIDGET-001 (Awesome Widget) - 42 in stock at $29.99"
}| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool queries a live platform (e-commerce) and returns specific fields. It does not explicitly state read-only behavior or potential side effects, but the provided information is sufficient for safe use.
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 well-structured with a clear first sentence, bullet points for args and returns, and an example. It is slightly verbose but maintains clarity.
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 single parameter and the presence of an output schema, the description covers input, output structure, and an example comprehensively, leaving no gaps.
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 0%, but the description provides a thorough explanation of the 'sku' parameter, including its meaning and example, fully compensating for the lack of schema description.
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 checks real-time inventory, price, and shipping for a product SKU, using a specific verb and resource. It distinguishes itself from siblings like get_price (which only returns price) and get_product_details (which returns more general details).
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 obtaining stock, price, and shipping availability, but does not explicitly state when not to use it or compare to alternatives. However, the context is clear enough for an agent to select appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_priceAInspect
Get current price for a product SKU.
Args:
sku: Product SKU - e.g., "WIDGET-001"
Returns:
Dictionary with sku and current price
Example:
>>> await get_price("WIDGET-001")
{"sku": "WIDGET-001", "price": 29.99, "currency": "USD"}| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It correctly indicates a read operation and provides return format, but lacks details on error handling or data freshness.
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 extremely concise: one line summary, then structured Args/Returns/Example. No wasted 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?
Given the simplicity of the tool (one parameter, straightforward output), the description is complete. It explains the parameter, the return format with an example, and is sufficient for the agent to use.
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 description coverage is 0%, but the description adds meaning with an example and format 'Product SKU - e.g., "WIDGET-001"', compensating for the lack of schema documentation.
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 'Get current price for a product SKU', which is a specific verb and resource. It distinguishes itself from siblings like check_stock and get_product_details.
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?
No explicit guidance on when to use this vs alternatives. The sibling tool names imply different purposes, but the description itself does not provide usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_product_detailsBInspect
Get full product details optimized for AI agents (structured JSON).
Args:
sku: Product SKU - e.g., "WIDGET-001"
Returns:
Dictionary with catalog fields; ``found`` is False when the SKU is missing.| Name | Required | Description | Default |
|---|---|---|---|
| sku | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions return structure and 'found' field, missing details on errors, permissions, or side effects.
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?
Three sentences, front-loaded with purpose, no wasted 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?
Adequate for a simple one-param tool with output schema, but lacks usage guidance and behavioral details.
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 description coverage is 0%; description adds example and basic identification for 'sku', but minimal elaboration.
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 'Get' and resource 'full product details', distinguishing from siblings like check_stock, get_price, list_products, and search_products.
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?
No guidance on when to use this tool versus alternatives; lacks context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_productsAInspect
List products from the connected store, paginated.
Use this tool when an agent needs to DISCOVER products by browsing the
catalog rather than VERIFYING a known SKU. The response includes the SKU
for every product, so a follow-up ``check_stock(sku)`` or
``get_product_details(sku)`` is a natural next step.
Args:
limit: Number of products to return (1-50, default 10).
cursor: Opaque cursor from a previous response's ``next_cursor``.
Omit for the first page.
Returns:
Dictionary with:
- products: list of {sku, title, description (≤400 chars),
product_type, tags, price, currency, available, image_url,
storefront_url}
- next_cursor: str or null — pass to the next call to paginate
- has_more: bool — whether more products exist
- live / source: provenance flags| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| cursor | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses pagination behavior (cursor, limit), return fields, and that it's a read operation (listing products). It does not mention authentication or rate limits, but given the nature of a list tool, it is fairly transparent. Score is 4 because it is detailed but lacks some potential side-effect info (though unlikely for a list).
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 well-structured: a concise opening sentence, usage guidance, then parameter descriptions, and finally return value details. It is front-loaded with the core action. Minor redundancy (e.g., 'paginated' is repeated), but overall 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?
Given that there is an output schema (detailed return structure) and only two parameters, the description is quite complete. It explains pagination, field composition, and provenance flags. It lacks explicit error handling or empty result behavior, but for a browsing tool this is acceptable.
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 has 0% coverage (no parameter descriptions). However, the description explains both parameters: limit indicates range 1-50 and default 10, cursor is described as opaque and how to use it (omit for first page). This adds significant meaning beyond the bare 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?
The description clearly states 'List products from the connected store, paginated.' It specifies the action, resource, and pagination behavior. It distinguishes itself from siblings by contrasting discovery (browsing catalog) against verifying a known SKU, which is the purpose of other tools like check_stock and get_product_details.
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 states when to use: 'when an agent needs to DISCOVER products by browsing the catalog rather than VERIFYING a known SKU.' It also suggests natural follow-up steps with check_stock or get_product_details, providing clear context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsAInspect
Search products in the connected store by keyword.
Use this when a shopper's query suggests specific terms the agent can
match against product titles or tags — e.g. "HEPA air purifier" or
"leather wristwatch". Matches Shopify's native storefront search
behavior, so results align with what customers would find on the site.
Args:
query: Keyword or phrase to match.
limit: Max products to return (1-50, default 10).
Returns:
Same shape as ``list_products``. Empty products list when no matches.| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description explains behavior: matches keyword against titles/tags, aligns with Shopify search, returns same shape as list_products, empty list on no matches. Lacks explicit statement on read-only nature or side effects.
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?
Approximately 100 words, front-loaded with purpose, includes bulleted Args for clarity, no redundant information. Every sentence adds value.
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 an output schema exists, the description mentions return shape and empty list behavior. Covers the main use case, but lacks details on pagination or sorting.
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 description coverage is 0%, but the description includes an Args section explaining the query parameter and limit with default and range, adding significant meaning beyond the raw 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?
The description clearly states it searches products by keyword, specifying the action and resource. It distinguishes from siblings like list_products by focusing on keyword matching against titles/tags, which is a specific use case.
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 says to use when a shopper's query suggests specific terms and gives examples. It implies that for general listing the sibling list_products is more appropriate, but doesn't name alternatives explicitly.
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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