agentic-shelf
Server Details
Agentic commerce MCP: live Shopify catalog + inventory + AI citation measurement
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 5 of 5 tools scored. Lowest: 3.5/5.
Multiple tools have unclear boundaries and significant functional overlap. check_stock already returns price information, making get_price redundant. get_product_details and check_stock both retrieve product data for a known SKU, creating confusion about which to use for what purpose. The descriptions attempt to differentiate them, but their core functionality overlaps substantially.
The naming follows a mostly consistent verb_noun pattern with clear, descriptive names. check_stock, get_price, get_product_details, list_products, and search_products all follow this convention. The minor deviation is that 'check_stock' uses 'check' while others use 'get' or 'list', but the pattern remains readable and predictable.
Five tools is a reasonable count for an e-commerce inventory/catalog server. This provides enough functionality for basic operations without being overwhelming. The scope aligns well with the domain, though some tools could potentially be consolidated to reduce redundancy.
The toolset covers read operations well but lacks write/update capabilities. For an e-commerce domain, there are notable gaps in CRUD coverage - no tools for creating, updating, or deleting products, managing orders, or handling inventory adjustments. The surface is complete for browsing and checking but incomplete for full lifecycle management.
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?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: real-time querying, connection to e-commerce platforms (Shopify, WooCommerce), and return of specific fields (stock, price, shipping availability). However, it does not mention potential limitations like rate limits, authentication needs, or error handling.
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 and appropriately sized, with a clear purpose statement, detailed explanation, args and returns sections, and an example. Every sentence adds value, and it is front-loaded with the core functionality.
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 tool's complexity (simple query), no annotations, and an output schema (implied by the returns section), the description is complete. It covers purpose, usage, parameters, return values, and provides an example, leaving no significant gaps for the agent to understand and invoke the tool correctly.
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% description coverage, but the description fully compensates by explaining the 'sku' parameter with a clear definition ('Product SKU (Stock Keeping Unit)'), an example ('RED-WIDGET-001'), and its role in the tool. 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 the tool's purpose with specific verbs ('check real-time inventory, price, and shipping') and resource ('product SKU'), distinguishing it from siblings like 'get_price' (price only) and 'get_product_details' (broader details). It explicitly mentions the e-commerce platform context.
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 clear context for when to use this tool ('check real-time inventory, price, and shipping for a product SKU'), but does not explicitly state when not to use it or name alternatives like 'get_price' or 'get_product_details' from the sibling list. The context is sufficient but lacks explicit exclusions.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only operation by using 'Get', but doesn't specify if it requires authentication, has rate limits, or handles errors. The example shows a return format, which adds some context, but key behavioral traits like performance or side effects are missing.
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 and front-loaded, starting with the core purpose, followed by args, returns, and an example. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
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 tool's low complexity (one parameter) and the presence of an output schema (implied by 'Returns' section), the description is mostly complete. It covers the purpose, parameter semantics, and return format. However, it lacks usage guidelines and some behavioral details, which prevents a perfect score.
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 description adds meaningful context beyond the input schema, which has 0% coverage. It explains that 'sku' is a 'Product SKU' and provides an example value ('WIDGET-001'), clarifying the parameter's purpose and format. Since there's only one parameter, this is sufficient to compensate for the schema's lack of descriptions.
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 purpose with a specific verb ('Get') and resource ('current price for a product SKU'), making it immediately understandable. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_product_details' or 'check_stock', which might also provide pricing information, so it falls short of a perfect score.
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 no guidance on when to use this tool versus alternatives like 'get_product_details' or 'list_products'. It lacks any context about prerequisites, such as whether the SKU must exist, or exclusions, leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_product_detailsAInspect
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: it returns 'structured JSON' and includes a 'found' field to indicate missing SKUs, which clarifies error handling. However, it lacks details on permissions, rate limits, or other behavioral traits like whether it's read-only or has 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?
The description is appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for 'Args' and 'Returns'. Each sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
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 tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose, parameter semantics, and return structure adequately. However, it could improve by adding usage guidelines or more behavioral context to fully compensate for the lack of annotations.
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% description coverage, so the description must compensate. It adds meaningful semantics: it explains that 'sku' is a 'Product SKU' and provides an example ('e.g., "WIDGET-001"'), which clarifies the parameter beyond the schema's basic type. Since there's only one parameter, this is sufficient for good understanding.
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 purpose: 'Get full product details optimized for AI agents (structured JSON).' It specifies the verb ('Get') and resource ('full product details'), and distinguishes it from siblings by focusing on detailed structured data rather than stock checks, pricing, or listing/searching. However, it doesn't explicitly differentiate from 'get_price' or 'search_products' in terms of depth vs. breadth.
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 no guidance on when to use this tool versus alternatives like 'check_stock', 'get_price', 'list_products', or 'search_products'. It mentions the tool is 'optimized for AI agents' but does not specify use cases, prerequisites, or exclusions, leaving the agent to infer usage from the purpose alone.
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?
With no annotations provided, the description carries full burden and delivers comprehensive behavioral information. It discloses pagination behavior, response structure, return value details (including specific fields like SKU, title, price), and follow-up action patterns. It also mentions 'provenance flags' which aren't obvious from the schema.
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 perfectly structured with clear sections: purpose statement, usage guidelines, parameters explanation, and return value details. Every sentence adds value with zero redundancy. It's appropriately sized for a tool with pagination and rich return data.
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 list/pagination tool with 2 parameters and rich output schema, the description is complete. It covers purpose, usage context, parameter semantics, return structure, and behavioral patterns. The presence of an output schema means the description doesn't need to fully document return values, but it still provides helpful context about the response fields and pagination flow.
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?
Despite 0% schema description coverage, the description fully compensates by explaining both parameters: 'limit' (number of products with range 1-50 and default 10) and 'cursor' (opaque cursor from previous response, omit for first page). It provides context about pagination flow that the schema alone wouldn't convey.
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 ('List') and resource ('products from the connected store'), and distinguishes it from sibling tools by specifying it's for 'DISCOVER products by browsing the catalog rather than VERIFYING a known SKU'. This provides specific differentiation from tools like check_stock or 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?
The description explicitly states when to use this tool ('when an agent needs to DISCOVER products by browsing the catalog rather than VERIFYING a known SKU') and provides clear alternatives ('follow-up check_stock(sku) or get_product_details(sku) is a natural next step'). It also distinguishes from search_products by emphasizing browsing vs. targeted searching.
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?
With no annotations provided, the description carries full burden and does well by explaining the search behavior ('matches Shopify's native storefront search behavior'), return format ('same shape as list_products'), and edge case handling ('empty products list when no matches'). It doesn't mention rate limits or authentication requirements, but provides substantial operational context.
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 efficiently structured with purpose statement, usage guidelines, parameter explanations, and return information in four distinct sections. Every sentence adds value with no redundancy or 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 tool has an output schema (returns same shape as list_products), the description doesn't need to explain return values. It provides complete context for a search tool: purpose, usage guidelines, parameter semantics, and behavioral transparency. The description stands well on its own despite no annotations.
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%, so the description must compensate. It provides clear semantic meaning for both parameters: 'query' is explained as 'keyword or phrase to match' with examples, and 'limit' gets range and default values (1-50, default 10). This adds significant value 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 the tool's purpose with specific verb ('search') and resource ('products in the connected store'), and distinguishes it from siblings by mentioning it matches Shopify's native storefront search behavior. It explicitly differentiates from list_products by being keyword-based rather than general 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?
The description provides explicit guidance on when to use this tool ('when a shopper's query suggests specific terms'), gives concrete examples ('HEPA air purifier', 'leather wristwatch'), and implicitly distinguishes it from list_products by emphasizing keyword matching versus general listing.
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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