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wendys_restaurant

Read-only

Wendy's restaurant detail by numeric store id (e.g. from wendys_nearby): address, coordinates, phone, hours, and whether carry-out/dine-in/drive-thru are physically offered plus feature flags (wifi, mobile order/pay, Coke Freestyle, digital coupons, loyalty, gift cards).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "store_id": "<store_id>"
    },
    "name": "wendys_restaurant"
  }
}

Input Schema

NameRequiredDescriptionDefault
store_idYesRequired. Numeric store id, e.g. from /wendys/nearby's stores[].store_id.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds useful context by distinguishing physical service availability (carry-out/dine-in/drive-thru 'physically offered') from feature flags, a nuance an agent could not infer from the schema or annotations alone.

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?

A single front-loaded sentence leads with purpose and lookup key before the attribute list, with no filler. The long parenthetical feature enumeration is dense but each item is informative rather than padding.

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?

With one fully documented required parameter, an output schema covering return values, and annotations covering the safety profile, almost nothing is left for the description to carry. It goes slightly beyond requirements by describing content, leaving only error handling for an invalid id unaddressed.

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% and the schema already states 'Numeric store id, e.g. from /wendys/nearby's stores[].store_id.' The description's 'numeric store id (e.g. from wendys_nearby)' essentially restates that, so the baseline 3 applies.

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 names a specific resource (Wendy's restaurant detail) and the exact lookup key (numeric store id), then enumerates the returned attributes, so an agent knows precisely what it gets. It does not differentiate from the sibling wendys_store or wendys_directory, which is the only thing keeping it from 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 Guidelines3/5

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

Citing 'e.g. from wendys_nearby' implicitly signals the preceding step in the workflow (find a store, then fetch its detail). However, there is no explicit statement of when to choose this over wendys_store or wendys_directory, so usage is only implied.

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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