Crawlora MCP

wendys_menu

Read-only

Returns every item in one category of Wendy's national menu: id, name, slug, Wendy's own displayed price and calorie strings, whether it is a combo (its GET /wendys/item response returns combo_slots instead of components/variants), a limited-time-offer flag, and whether it has required customization (e.g. a salad's dressing choice). Category slugs come from GET /wendys/categories. Use wendys_categories to list Wendy's national menu categories.

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": {
      "category": "<category>"
    },
    "name": "wendys_menu"
  }
}

Input Schema

Name	Required	Description	Default
`category`	Yes	Required. Category slug from /wendys/categories.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=true), so the description doesn't need to restate them. It adds genuine behavioral context beyond annotations: that combo items return combo_slots instead of components/variants and that some items carry required customization. Return-field behavior is well disclosed.

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?

The core purpose is front-loaded and each sentence carries information. It is somewhat dense with parenthetical explanations, but nothing is wasted; the item-attribute list is directly useful for an agent deciding to call it.

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 single-parameter tool with full schema coverage, an output schema, and read-only annotations, the description is complete: it defines scope, names the slug source, and routes to the correct sibling for categories. No missing information an agent would need to invoke it correctly.

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%, and the schema already documents that 'category' is a required slug from /wendys/categories. The description reinforces the source and scopes it to the national menu, but adds little syntax or format detail beyond the schema. Baseline 3 is appropriate.

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?

States a precise verb ('Returns') and resource ('every item in one category of Wendy's national menu'), then enumerates the returned fields. An agent can distinguish it from wendys_item (single-item detail) and wendys_categories (category list) without opening any schema.

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?

Explicitly routes the agent to wendys_categories for the required category slugs and states the slug source (GET /wendys/categories), giving clear prerequisite context. It stops short of naming when to prefer wendys_item or wendys_store_menu instead, so it lacks full when-not guidance.

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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