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kroger_suggest

Fetch Kroger search-box suggestions for any partial query, or return trending shopping shortcuts when the query is empty; optionally scope results to a store location.

Instructions

Get Kroger search-box suggestions. Returns Kroger's own search-box suggestions for a (possibly empty) query, sourced directly from Kroger's public suggestions API rather than the rendered search page. An empty query returns Kroger's default "trending" shopping shortcuts instead of an error. location_id scopes results to one Kroger store and defaults to a confirmed-working store id when omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoPartial search text; omit for trending default suggestions
location_idNoKroger store/location id

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden. It adds valuable edge-case behavior: an empty query returns trending shortcuts rather than an error, and location_id defaults to a confirmed-working store when omitted. It also clarifies the data source is the public suggestions API rather than the rendered search page. It does not mention rate limits or output format, but for a simple read-only suggest tool this is reasonable coverage.

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 three sentences with no filler. The core purpose is front-loaded, and every sentence adds useful information: the data source, the empty-query behavior, and the location_id default. It is compact without sacrificing clarity.

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 a simple two-parameter tool with no output schema and no annotations, the description covers the essential behavioral nuances: optional query behavior, location scoping, and defaulting. It does not specify the exact return shape, which would be helpful, but the phrase 'Returns ... suggestions' gives enough guidance for an agent to invoke the tool correctly.

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 description coverage is 100%, so the schema already documents both parameters. The description adds meaning beyond the schema by explaining that an empty query triggers default trending suggestions and that location_id scopes results to a single store and has a sensible default. This goes beyond the baseline for well-covered parameters.

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 clearly states the verb and resource: 'Get Kroger search-box suggestions' and further explains it returns Kroger's own suggestions for a query. It is specific enough to be distinguished from the many sibling suggest tools because it names Kroger explicitly and describes the data source. However, it does not explicitly differentiate itself from direct sibling tools like kroger_search, so it falls just short of a 5.

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 description gives clear context on when to use the tool: when an agent needs Kroger search-box suggestions, including for empty queries which return trending shortcuts. It does not explicitly state when not to use it or name alternatives such as kroger_search, but the context is strong enough for an agent to infer the primary use case.

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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