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JavaScript
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burgerking_menu

Fetch a specific Burger King restaurant's full menu grouped by category, with delivery and pickup prices, calories, and allergens by store ID.

Instructions

Get one Burger King restaurant's full priced menu. Returns one restaurant's full menu grouped into categories (e.g. "Breakfast Sandwiches", "Flame Grilled Burgers"). Every entry is an item, a combo, or a picker (a variant-choice product such as "choose your drink size") and carries a delivery and pickup price -- these genuinely differ -- plus calories, detailed nutrition facts and flagged allergens when Burger King publishes them, and an image. Prices and availability reflect this specific restaurant, not a national default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketNoRBI market the store belongs to, one of `US`, `CA` (default `US`)
store_idYesBurger King's numeric store id, from /burgerking/locations

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It explains what kinds of entries appear, that delivery and pickup prices differ, and that nutrition/allergen data appears only when Burger King publishes it. A slight gap is the absence of pagination or error-behavior notes, but for a read-only menu fetch the coverage is solid.

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 dense but front-loaded with the core action and result shape, then expands into useful detail. It is slightly longer than the minimum, but every sentence adds value and there is no repetitive 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 must summarize the return shape; it does so thoroughly by specifying menu structure, entry types, pricing differences, nutrition/allergen data, and images. The store_id source is supplied in the schema, and the per-restaurant scope is emphasized, leaving the agent enough information to select and invoke the tool 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 description coverage is 100%, and the schema already explains market and store_id, including where store_id originates. The description itself adds no new parameter-level semantics, so a 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?

Opens with a specific verb and resource: 'Get one Burger King restaurant's full priced menu.' It clearly scopes to one restaurant, says the menu is grouped into categories, and names what the entries carry, which distinguishes it from sibling tools like burgerking_product and burgerking_availability.

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 this is for a single restaurant's full menu and that prices/availability are per-store, not a national default. It does not explicitly name the sibling tools to choose instead, so the guidance is strong but not a complete exclusion-based routing.

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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