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Crawlora MCP

Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

taco_bell_menu

Fetch one Taco Bell menu category's items with prices, calories, product codes, availability, and customization flags using a category slug.

Instructions

List one Taco Bell menu category's items with prices and calories. Returns the items in one Taco Bell US menu category, each with its product code, name, price, calorie count, product and food type, image, and flags for whether it is customizable, has a meatless variant, and is currently available in store. Category slugs come from GET /taco-bell/categories, and the endpoint accepts either the bare slug (tacos) or the full path form (/food/tacos). Taco Bell is one of the few brands publishing both price and calories in the same payload.

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": "taco_bell_menu"
  }
}

Input Schema

NameRequiredDescriptionDefault
categoryYesCategory slug from /taco-bell/categories

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does this well by enumerating exactly what each returned item contains: product code, name, price, calorie count, product/food type, image, and flags for customization, meatless variant, and availability. It also adds the useful note about price and calories in one payload. It does not mention pagination, response envelope, or error cases, but for a single-category list tool this is a minor gap.

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 description is one focused paragraph of three sentences, front-loading the core purpose and then detailing return fields and input format. The final sentence about Taco Bell being one of the few brands with price and calories is contextual flavor rather than invocation-critical, but it is short and does not detract much.

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 no output schema and no annotations, the description covers what would normally be in the output schema: the exact fields returned for each item. It also covers the parameter format and the source of valid slugs. It stops short of noting any limits or response-level details, but the tool is a straightforward single-category read, so this is adequate.

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 coverage is 100%, but the description goes beyond the schema and adds genuinely useful semantics for the sole 'category' parameter: it specifies that slugs come from a sibling endpoint and that the endpoint accepts both bare slug ('tacos') and full path ('/food/tacos') forms. This meaningfully reduces the chance of an agent passing a malformed or invalid value.

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 description states a specific action ('List'), resource ('one Taco Bell menu category's items'), and the key data points ('prices and calories'). It explicitly limits scope to 'one Taco Bell US menu category,' which distinguishes it from siblings like taco_bell_categories, taco_bell_product, and taco_bell_store_menu. An agent can immediately understand what this tool does.

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 a clear prerequisite: 'Category slugs come from GET /taco-bell/categories.' It also explains the acceptable input forms ('bare slug' or 'full path form'), which is practical invocation guidance. It does not explicitly name alternative tools to consider instead, but the scope is clear enough to route most usage correctly.

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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