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pizzahut_modifiers

Get a Pizza Hut product variant's full customization tree: every slot, modifier, and placement option with real price deltas. Use variant_code from the menu for detailed extras pricing.

Instructions

Get one Pizza Hut product variant's full customization tree. Returns one product variant's full build-your-own customization tree -- every slot (e.g. "Pizza Sauce", "Pizza Cheese", "Pizza Toppings", "Crust Finishers"), every modifier within it (a specific topping, sauce flavor, or seasoning), and every weight/placement option (e.g. Light, Regular, Extra, or a Left/Right/Whole pizza-half placement) with its own real price delta -- zero for an amount already included by default, a positive upcharge for an "extra" option. variant_code comes from /pizzahut/menu's variant_code or variants[].variant_code fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelNoOrder channel the customization tree and pricing reflect (default WEB)
store_numberYesPizza Hut's store number, from /pizzahut/stores
variant_codeYesA product's specific size/crust/style variant code, from /pizzahut/menu

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / channel / enum
      Added value: +[
+  "WEB",
+  "MOBILE",
+  "POS",
+  "KIOSK",
+  "DOORDASH",
+  "UBEREATS",
+  "GRUBHUB",
+  "IOS",
+  "ANDROID",
+  "DELIVEROO",
+  "JUST_EAT",
+  "CALL_CENTER"
+]
  2. Addedv1.16.2

TDQS

A4.3/5.0
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. It explains what the tool returns: slots, modifiers, weight/placement options, and price deltas, and clarifies the price delta semantics (zero for included, positive for extra). This gives the agent a clear behavioral model without relying on structured fields.

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 a single long sentence but is well-organized, front-loading the main purpose before elaborating on return structure and input source. Every clause contributes useful information, with no fluff or redundancy.

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?

For a read-only retrieval tool with no output schema and no annotations, the description covers the return structure, input parameter origins, and pricing semantics. It lacks explicit mention of authentication or rate limits, but those are not essential for an agent selecting and calling this tool.

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 parameters are already documented. The description adds context beyond the schema by explaining where variant_code originates and that channel defaults to WEB. This extra guidance helps the agent correctly source the input.

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 clearly states the tool retrieves the full customization tree for a single Pizza Hut product variant. It specifies the exact resource (variant customization tree) and differentiates it from sibling tools like pizzahut_menu by focusing on a specific variant's modifiers and pricing.

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 explicitly tells the agent that variant_code comes from /pizzahut/menu's fields, which indicates this tool is a follow-up after fetching the menu. It does not explicitly state when not to use it or name alternatives, but the source of the required parameter makes the use case clear.

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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