king-soopers-mcp

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find_stores

Discover King Soopers grocery stores near any US zip code within a chosen radius. Get store locations and details for your area quickly.

Instructions

Find King Soopers stores near a zip code.

Results are ordered by the API's own proximity ranking. Each store carries a search_scope string describing this search, for example 'within 10 mi of 80202'; the Kroger locations endpoint returns no per-store distance and no coordinates for the search centre, so no distance is reported per store.

Args: zip_code: 5-digit zip to search around. Falls back to KROGER_DEFAULT_ZIP. radius_miles: Search radius, 1-100 miles. limit: Maximum number of stores to return, 1-200.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_stores"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`zip_code`	No
`radius_miles`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 6, 2026

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden and excels: it discloses API-side ordering, the search_scope string, the absence of per-store distance and search-center coordinates, and the zip-code fallback. These are non-obvious behavioral details that an agent would otherwise only learn at runtime.

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 front-loads the core purpose, then uses compact sections for behavioral caveats and parameter documentation. Every sentence provides decision-relevant detail with no filler.

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?

The description covers purpose, all parameters, defaults/fallbacks, ordering, and key output quirks. Since an output schema is present, the description is appropriately complete for a read-only lookup tool.

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 description coverage is 0%, but the Args section fully compensates by explaining zip_code format and fallback, radius_miles range (1-100), and limit range (1-200). This adds units, constraints, and default behavior that the bare integer schema does not convey.

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 opening sentence states a specific verb ('Find'), a specific resource ('King Soopers stores'), and the search criterion ('near a zip code'). This makes the tool's purpose immediately clear and distinguishes it from siblings like search_products and set_default_store without needing to name them.

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: use this tool when you need nearby King Soopers stores for a zip code, and it documents the fallback to KROGER_DEFAULT_ZIP. However, it does not explicitly mention alternatives or say when not to use it, which prevents a full 5.

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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