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JavaScript
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kroger_search

Search Kroger products by keyword for normalized cards with price, brand, size, stock, and facets. Filter by brands, nutrition, savings, price, scent, or flavor; sort by relevance, name, or popularity.

Instructions

Search Kroger products. Searches Kroger products by keyword and returns normalized 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). Served from Kroger's own search JSON API with real upstream pagination; if that path is unavailable it falls back to parsing the rendered search page, and the source field reports which path answered. Facet filters and sort apply to the JSON path only: when any of them is set, a JSON-path failure 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

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries full disclosure burden. It transparently explains the fallback from JSON API to parsed page, the source field indicating which path answered, and the conditional error behavior when filters/sort are set. This goes far beyond a generic search description.

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?

The description is efficiently structured, front-loading the purpose and return content before explaining backend behavior. Each sentence adds distinct value with 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 tool with 11 parameters and no output schema, the description covers essential invocation details: return format, facet groups, pagination behavior, fallback path, and filter-specific error semantics. No critical information is missing for correct use.

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 coverage is 100%, so all 11 parameters are documented in the schema. The description adds some behavioral nuance (e.g., filters apply only to JSON path, price_min defaults to 0) but does not add per-parameter semantic beyond the schema descriptions. Meets baseline for covered schema.

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?

Clearly states the tool searches Kroger products by keyword and returns normalized product cards with specific attributes (price, unit price, brand, size, stock level) plus facet groups. This distinguishes it from sibling tools like kroger_category (browse categories) or kroger_product (individual product lookup) via the keyword-search focus.

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?

Provides context on when to use (keyword search) and explains that facet filters/sort only work on the JSON path, with a specific error case when those are set. Does not explicitly name alternatives or exclusions, but the keyword distinction implies appropriate usage versus browsing tools.

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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