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Crawlora MCP

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

pizzahut_store

Look up one Pizza Hut restaurant by store number to retrieve its open hours per order channel, order limits, tipping, and accepted payment methods.

Instructions

Get one Pizza Hut restaurant by its store number. Returns one Pizza Hut restaurant's detail by its exact store number, from /pizzahut/stores, including its recurring open hours per order channel (carryout, delivery, dine-in, drive-thru, catering carryout) plus each channel's ordering constraints -- minimum/maximum order amount, whether tipping is offered, and accepted payment methods. A channel this restaurant does not offer at all is omitted from hours rather than reported as always-closed.

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_number": "<store_number>"
    },
    "name": "pizzahut_store"
  }
}

Input Schema

NameRequiredDescriptionDefault
store_numberYesPizza Hut's store number, from /pizzahut/stores

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, the description carries the full burden of behavioral disclosure. It does so by explaining precisely what the response contains (recurring hours per channel, ordering constraints) and, importantly, states an unusual behavior: channels not offered are omitted entirely rather than shown as always-closed. It however does not mention error handling or side effects, but for a read-only single-store lookup this is sufficient.

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 front-loaded with the main purpose, then provides specific return content. There is minor redundancy between the first two sentences, both describing the tool as returning one restaurant by store number, but the additional details (order channels, constraints, omitted-channel note) are valuable and not extraneous. Overall it is well-structured and easy to parse.

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?

Given there is no output schema, the description does a good job of explaining what the agent will receive, including the fine-grained channels and the omission semantics. It could mention error behavior for invalid store numbers or the response structure, but for a low-complexity single-parameter read tool it covers what an agent needs to decide whether to invoke it.

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% for the single parameter, and the description repeats the schema's 'from /pizzahut/stores' context while adding 'exact' to reinforce the need for the precise identifier. Since the schema already explains the parameter fully, the description adds little semantic beyond that recurrence, so the baseline of 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?

The description begins with a clear, specific action 'Get one Pizza Hut restaurant by its store number,' which distinguishes it from sibling tools like pizzahut_stores (lists stores) and pizzahut_menu. It explicitly identifies the resource, its identifier, and the exact output scope, so an agent can immediately understand when to call it.

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 that input must be the exact store number and notes this number comes from /pizzahut/stores, implying the agent should already have the number from the list endpoint. It does not explicitly name alternative tools for related tasks (e.g., delivery estimates or menus), but the context of 'one exact store number' is enough to guide selection without exclusion.

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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