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JavaScript
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chipotle_restaurant_menu

Fetch a Chipotle restaurant's live menu with dine-in and delivery prices, plus item customization details, by restaurant number.

Instructions

Get one Chipotle restaurant's menu with prices. Returns one restaurant's live online menu split into entrees, sides, drinks and non-food items. This is the only Chipotle endpoint that carries prices: every item has both a dine-in price and a delivery price, which genuinely differ. Each item also carries its full customization tree -- contents (the individual fillings, salsas and sides that make it up, each with their own price pair) and content_groups (how many picks each group allows). Prices are per-restaurant, so two restaurants will legitimately return different numbers for the same item.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
restaurant_numberYesChipotle's numeric restaurant id
include_unavailableNoInclude items the restaurant currently has unavailable (default false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that prices are per-restaurant and that dine-in vs delivery prices differ, which is valuable behavioral context. It also mentions each item has a customization tree, setting expectations for nested data. However, it doesn't state potential lack of availability or that items might be missing if unavailable, but the include_unavailable parameter hints at that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise yet packed with useful information. It front-loads the core purpose and key differentiator (prices), then explains the customization tree and pricing behavior. Every sentence adds value; no fluff or repetition. The length is appropriate for the complexity of the tool.

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 the tool has 2 parameters, one required, and no output schema, the description explains the response structure (menu split into categories, items with prices, customization tree). It also covers the per-restaurant pricing nuance. However, it doesn't mention pagination or any limits, which might be relevant for large menus, but that might not be necessary given the focus on one restaurant.

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 the restaurant_number parameter implicitly (numeric id) and the include_unavailable parameter contextually by mentioning availability. It doesn't describe exact formats or defaults, but the schema already covers these. The description's mention of 'live online menu' adds nuance that could affect parameter usage.

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 fetches a specific restaurant's menu with prices, and emphasizes it's the only Chipotle endpoint with prices柳 This distinguishes it from siblings like chipotle_menu and chipotle_restaurant. The verb 'Get' and resource 'restaurant's menu' are specific, and the mention of it being the only price-carrying endpoint adds strong differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly notes this is the only endpoint with prices, guiding when to use it over other Chipotle endpoints. It also explains the per-restaurant pricing model, which helps agents understand when to filter by restaurant. However, it doesn't explicitly say when NOT to use it (e.g., for general menu without prices), but the differentiation is strong enough.

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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