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dunkin_menu

Fetch Dunkin's national menu with item names, descriptions, photos, and categories; no store parameter needed. Prices and nutrition are not included.

Instructions

Get Dunkin's national menu. Returns Dunkin's national menu: item names, descriptions and photos grouped into categories (Iced Drinks, Hot Drinks, Frozen Drinks, Sandwiches & Wraps, Savory Bites, Donuts & Bakery, Snacks and Sides). Dunkin publishes one shared menu across every US store, so this takes no store parameter. Does not carry prices or nutrition -- pricing varies by market/franchisee and isn't part of this content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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?

With no annotations present, the description carries the full burden of behavioral disclosure. It does well by stating not only what is returned but also what is not included (prices and nutrition) and why no store parameter exists. It is a purely informational read tool, making the absence of side-effect disclosure acceptable.

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 well front-loaded with the purpose, and the category list earns its place by clarifying the exact data content. It is slightly redundant with 'Get Dunkin's national menu' and 'Returns Dunkin's national menu' in the first two sentences, but overall every sentence contributes useful scoping information.

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?

For a zero-parameter, no-output-schema read tool, the description is complete: it fully states the returned content, the category structure, the non-store-specific scope, and what the data does not contain. An agent has all the information needed to call this tool correctly without ambiguity.

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?

The tool has zero parameters, so per the rubric the baseline is 4. The description adds meaningful context by explaining that there is no store parameter because Dunkin publishes one shared national menu, which clarifies why the empty input schema is correct and prevents an agent from expecting location-based arguments.

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 opens with a specific verb and resource, 'Get Dunkin's national menu,' and then enumerates exactly what is returned: item names, descriptions, photos, and categories. It distinguishes itself from Dunkin's store/location tools by explicitly noting it takes no store parameter and is a national-level menu.

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 provides clear usage context by stating that the menu is shared across every US store and therefore no store parameter is needed, which implicitly routes store-specific lookups to sibling tools like dunkin_store or dunkin_directory. It also explicitly excludes prices and nutrition, helping an agent avoid using it for those needs. It stops short of naming alternative tools explicitly.

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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