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sephora_search

Search Sephora's product catalog by keyword with pagination, returning normalized brand, price, rating, and review data; supports brand, price range, and facet filters.

Instructions

Sephora product search. Searches Sephora's product catalog by keyword, with real page-based pagination. Returns normalized products with brand, pricing, rating, and review count. Sephora's own search never returns a genuine zero-result state for a nonempty keyword -- an unrecognized/nonsense keyword still returns a full, unrelated fallback result set rather than an empty one. price_min/price_max must be provided together (whole dollars) -- upstream silently ignores a one-sided price range rather than filtering or erroring, so this endpoint rejects a one-sided range as invalid instead of passing it through. brand and filter each accept multiple values (OR'd together within the same facet); brand/rating_min/is_new/filter/price_min/price_max can all be combined with each other (AND'd together across different facets).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoResult page, 1-based, defaults to 1
brandNoOne or more exact brand names to filter to (OR'd together); repeat the param for multiple values
queryYesSearch keyword
filterNoAdditional facet:value filters, repeat the param for multiple; facet must be one of: benefits, ingredientpreferences, colorfamily, formulation, size, shoppingpreferences, agerange, skintype, finish, skinconcerns, coverage, hairtype, hairconcerns, hairtexture
is_newNoWhen true, filters to products flagged New
sort_byNoSort order, defaults to featured
page_sizeNoResults per page, 1 to 100, defaults to 60
price_maxNoMaximum price in whole dollars; must be set together with price_min
price_minNoMinimum price in whole dollars; must be set together with price_max
rating_minNoMinimum star rating, 1 to 4

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.17.5
    • addedInput schema / properties / brand / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / filter / items
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / sort_by / enum
      Added value: +[
+  "featured",
+  "top_rated",
+  "new",
+  "best_selling",
+  "price_low_to_high",
+  "price_high_to_low"
+]
  2. Addedv1.14.0

TDQS

A4.7/5.0
Behavior5/5

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

With zero annotations, the description carries the full behavioral burden and delivers: Sephora's never-empty fallback result quirk for nonsense keywords, the one-sided price-range rejection rationale (upstream silently ignores it), and the OR-within-facet / AND-across-facet combination semantics. These are exactly the non-obvious behaviors that would otherwise mislead an agent.

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?

Four dense sentences that are front-loaded with purpose, then return shape, then the critical behavioral quirks. Every sentence earns its place — the fallback quirk and price-validation rationale are operational necessities, not filler. No redundancy.

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?

For a search endpoint with 10 parameters, no annotations, and no output schema, the description covers every invocation-critical behavior: result shape, the nature of pagination, fallback behavior on nonsense queries, validation rules, and filter arithmetic. Minor gaps such as total-count exposure or rate limits are secondary and do not block correct use.

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 meaning beyond the schema: it explains why price_min/price_max must be paired (upstream silently ignores one-sided ranges while this endpoint rejects them) and specifies cross-facet AND combination, which the per-parameter schema notes only cover within-facet OR. This is genuine added value for correct invocation.

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 and resource — 'Searches Sephora's product catalog by keyword' — and states the return shape ('normalized products with brand, pricing, rating, and review count'). This clearly differentiates it from siblings like sephora_product, sephora_product_reviews, and sephora_suggest by establishing the keyword-search entry point.

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

Usage Guidelines4/5

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

The keyword-triggered usage context is explicit, and the facet-combination section tells the agent how to construct multi-parameter queries. However, no sibling tools are named and there are no explicit when-not-to-use conditions routing the agent toward sephora_category or sephora_product instead.

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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