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pizzahut_menu

Get any Pizza Hut store's full priced menu grouped by category, including product variants and uncategorized items. Prices reflect the specified order channel.

Instructions

Get one Pizza Hut restaurant's full priced menu. Returns one restaurant's full menu grouped into categories (e.g. "Pizza", "Wings", "Deals"). Every entry is a product, a bundle (a deal or combo), or a standalone priced variant -- type tells callers which -- and carries a price in USD cents plus a decimal dollar value, and an image when the upstream publishes one. A product item also lists variants: every other priced size/crust/style option beyond its default (e.g. a pizza's Personal Pan through Large Original Stuffed Crust). Prices reflect this specific restaurant and order channel, not a national default. channel selects which order channel the menu is priced for (web ordering by default); prices can genuinely differ by channel (e.g. a delivery-marketplace channel vs. web). Some restaurants also carry products with no category at all (e.g. individual dip cup flavors); these are returned separately as uncategorized_items rather than silently dropped.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelNoOrder channel the menu is priced for (default WEB)
store_numberYesPizza Hut's store number, from /pizzahut/stores

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and succeeds. It discloses that entries are typed (product, bundle, or standalone variant), that prices are in USD cents plus dollars, that images are included only when upstream provides them, that prices vary by channel, and that uncategorized items are returned separately rather than dropped.

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 key purpose is front-loaded, and every section covers a meaningful behavioral or semantic detail. The description is verbose in places, with some repeated emphasis on 'full menu' and long parentheticals, but the extra words mostly add clarity instead of 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?

There is no output schema, so the description carries the burden of explaining response content. It covers category structure, entry dispatch types, price formats, image availability, channel sensitivity, variants, and uncategorized handling. For a two-parameter read tool, the caller has the context needed to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaning by explaining that channel genuinely changes prices, giving real examples, and clarifying that the menu is specific to the requested restaurant rather than a national default. It does not enumerate the allowed channel values, keeping it from a 5.

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 starts with 'Get one Pizza Hut restaurant's full priced menu,' naming a specific verb, resource, and scope. It then strongly differentiates the result by explaining categories, entry types, pricing units, variants, and uncategorized items, so an agent can clearly tell this tool from generic menu tools.

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?

The context implies when to use this tool: when a caller needs a specific restaurant's full menu with channel-specific pricing. However, the description never explicitly names sibling alternatives such as pizzahut_bundle_choices, pizzahut_modifiers, or dominos_menu, nor does it state when not to use this tool.

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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