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kroger_search

Search Kroger products by keyword to get product cards with price, brand, size, and stock, plus facet groups for filtering by brand, nutrition, savings, and price. Supports pagination.

Instructions

Search Kroger products. Searches Kroger products by keyword and returns product cards (price, unit price, brand, size, stock level) plus the facet groups Kroger offers for the query (brands, nutrition, savings, price range and more). It supports real pagination, and the source field reports which path answered. Facet filters and sort apply to the primary path only: when any of them is set, a failure of that path returns an error rather than silently falling back to unfiltered results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoOne-based result page
sortNoResult order. One of: relevance, name_asc, popularity_desc
queryYesSearch keyword
scentNoComma-separated scent facet values
brandsNoComma-separated brand names to filter by, taken verbatim from a previous response's facets
flavorNoComma-separated flavor facet values
savingsNoComma-separated savings facet values
nutritionNoComma-separated nutrition/dietary facet values
price_maxNoUpper bound of the price filter; required to filter on price
price_minNoLower bound of the price filter; defaults to 0
more_optionsNoComma-separated more-options facet values

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "name_asc",
+  "popularity_desc"
+]
  2. Addedv1.16.2

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations the description carries the full burden, and it does well: it discloses that pagination is real, that a `source` field reports which path answered, and notably that facet/sort failures error out rather than silently returning unfiltered results. It omits auth/rate-limit context, so not a 5, but the fallback-vs-error disclosure is genuine behavioral value.

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

Conciseness4/5

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

Purpose is front-loaded in the first sentence, followed by return contents and then the pagination/fallback caveat. Three dense sentences with no filler; the technical caveat sentence earns its place but makes it slightly heavy for a single lead.

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

Completeness4/5

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

For an 11-parameter tool with no output schema, the description usefully covers return content (product cards + facet groups), pagination, the source field, and error semantics. That is nearly complete; only auth/rate-limit context is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 11 parameters and the enum. The description reinforces the facet-filter and sort behavior but adds no syntax or format detail beyond the schema, so the baseline 3 is appropriate.

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

Purpose4/5

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

The description gives a specific verb+resource ("Search Kroger products") and details what is returned (product cards with price/brand/size/stock plus facet groups). It is clear, but it does not explicitly distinguish itself from the many kroger_* siblings (kroger_product, kroger_categories, kroger_suggest), so an agent still has to infer the boundary.

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: keyword search plus optional facet filters, with no explicit "use this instead of X" routing and no prerequisites. The note that facet filters/sort apply only to the primary path hints at intended usage but doesn't tell the agent when to reach for this tool over a sibling.

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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