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sephora_product

Fetch full Sephora product details—every shade variant with price, availability, rating, review count, and recent reviews—by passing the exact product slug.

Instructions

Sephora product detail. Returns one Sephora product's full detail (every color/shade variant with its own price and availability, rating, review count, and a sample of recent reviews). product_id is the full product-page slug, e.g. lip-sleeping-mask-P420652 -- copy it from the path segment after sephora.com/product/ on any product page; unlike some other retailers, an arbitrary or partial slug does not resolve.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_idYesFull Sephora product-page slug

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses the return shape (all variants, each with price/availability, plus a review sample) and a concrete failure mode — arbitrary or partial slugs do not resolve. It omits auth needs, rate limits, and variant-selection behavior, but the key operational constraint is surfaced.

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?

Front-loaded with purpose, then return contents, then parameter guidance. Three sentences, no filler, each carrying distinct information.

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

Completeness5/5

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

No output schema and no annotations, so the description must cover return values and invocation constraints — it does both (enumerated return fields, exact slug requirement, failure mode). Nothing an agent needs to call it correctly is missing.

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

Parameters5/5

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

Schema coverage is already 100%, but the description adds real meaning beyond it: an example slug format (`lip-sleeping-mask-P420652`), the exact source of the value (path segment after sephora.com/product/), and a warning that partial slugs fail. This is precisely the value the parameter dimension rewards.

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?

States a specific verb and resource: 'Returns one Sephora product's full detail,' then enumerates exactly what comes back (shade variants with price/availability, rating, review count, recent reviews). This clearly separates it from sibling list/search tools like sephora_search and sephora_category without ambiguity.

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?

Usage is implied rather than stated: it explains how to obtain the required product_id from a product page, which signals when the tool is applicable, but it never names an alternative (e.g., sephora_search to find a product first) or states an explicit when-not-to-use condition.

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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