Get a pricing quote for a product at a specific quantity. Quantity must be a positive integer; quotes below the product MOQ are flagged belowMoq and indicative only.
| Name | Required | Description | Default |
|---|---|---|---|
| pid | Yes | Product ID | |
| locale | No | Locale for pricing | en-AU |
| quantity | Yes | Quantity to quote (positive integer) |
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 a key detail about quotes below MOQ being flagged as belowMoq and indicative only, which is not in the schema. However, it does not disclose return format, error conditions, or side effects, leaving some behavioral uncertainty.
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 with no filler. The first sentence immediately states the tool's purpose, and the second adds a critical constraint. Every word earns its place, making it highly concise and well-structured.
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 simplicity (3 parameters, no output schema, no annotations), the description covers the essential purpose and an important edge-case (belowMoq). It does not describe the return value structure, but the tool name and general context imply a quote object. This is adequate but not exhaustive.
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 already provides complete descriptions for all three parameters (pid, locale, quantity), achieving 100% schema coverage. The description adds nothing meaningful about parameter meaning beyond what the schema states, so a baseline score of 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 clearly states the tool's function with a specific verb and resource: 'Get a pricing quote for a product at a specific quantity.' It distinguishes itself from sibling tools like get_product and search_products by focusing specifically on pricing quotes.
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 on when to use the tool: when a pricing quote for a product at a specific quantity is needed. It does not explicitly state when not to use it or mention alternatives, but the purpose is specific enough that the usage context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or action, from product and brand lookups to editorial collections and regional delivery info. Even similar listing tools (categories, industries, aesthetics, intents, inspirations, on-trend) are differentiated by their specific names and descriptions.
The naming pattern is strongly consistent: lowercase with underscores, and almost all tools use the get_ prefix plus a resource name. The only deviation is search_products, which uses 'search' instead of 'get', but it still follows the verb_noun structure.
With 21 tools, the server is on the heavy end of the typical range. The breadth is justified by the many distinct content types (products, brands, editorials, company info, reviews, regions, FAQ), but the count feels slightly bloated compared to leaner MCP servers.
The tool surface covers the full research and selection journey: product search, details, quotes, FAQ, company credibility, delivery areas, and inspirational content. The main gap is the lack of any transactional or order-placement tool, but that appears outside the intended scope.
