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JavaScript
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kyliecosmetics_product_recommendations

Fetch product recommendations by product handle for Kylie Cosmetics, returning related or complementary items for cross-sell and discovery.

Instructions

List Kylie Cosmetics product recommendations. Returns normalized recommended products for one Kylie Cosmetics (https://www.kyliecosmetics.com) product handle. The route handle is resolved to a Shopify product id before fetching recommendations. The storefront URL is fixed server-side.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum products, defaults to 10 and supports up to 20
handleYesProduct handle
intentNoRecommendation intent. Allowed values: related, complementary

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / intent / enum
      Added value: +[
+  "related",
+  "complementary"
+]
  2. Addedv1.16.0

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden. It usefully discloses that results are normalized, the handle is resolved to a Shopify product id, and the storefront URL is fixed server-side. However, it does not describe return shape, default intent behavior, or any failure/error considerations, leaving meaningful gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences with no filler. The core purpose is front-loaded, and the implementation details are packed efficiently without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter read tool this is workable, but the lack of an output schema and absence of any description of the response shape leaves 'normalized recommended products' vague. The description also does not clarify what happens when intent is omitted, which an agent needs to know for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful context beyond the schema by explaining that the handle is resolved to a Shopify product id and that the storefront URL is fixed server-side. Limit and intent are adequately covered by their schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-resource pair, 'List Kylie Cosmetics product recommendations,' and clarifies it operates on a single product handle. It clearly differentiates this from sibling tools like kyliecosmetics_product or kyliecosmetics_products by framing the output as recommendations for one product.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly states the tool is for one Kylie Cosmetics product handle and notes the storefront URL is fixed server-side, implying the agent only needs to supply a handle. However, it does not explicitly contrast this with alternatives such as kyliecosmetics_product or other product recommendation tools, nor does it state when not to use it.

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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