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chipotle_restaurant_meals

Retrieve restaurant-specific Chipotle meal prices and details. Shows preset meals with dine-in/delivery pricing, calories, macros, and components for Build-Your-Own, High Protein, and Influencer lines.

Instructions

List one Chipotle restaurant's preset meals with prices. Returns the preset meals one Chipotle restaurant sells -- group Build-Your-Own packs, the High Protein line and limited-time Influencer meals -- each with its dine-in and delivery price at that restaurant, calorie label, dietary and macro tags, the components that make it up, merchandising tags and images. Prices are genuinely restaurant-specific: the same Build-Your-Own Chicken pack is priced differently from one location to another, so this is the endpoint to use for real pricing rather than /chipotle/meals, which lists the national meal definitions with no prices at all. Calorie labels are a range for build-your-own meals and a single figure for fixed ones, so they are returned as strings. Restaurant numbers come from GET /chipotle/restaurants.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
meal_typeNoFilter to one meal family. One of BuildYourOwn, HighProtein, Influencer.
restaurant_numberYesChipotle's numeric restaurant id, from /chipotle/restaurants

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / meal_type / enum
      Added value: +[
+  "BuildYourOwn",
+  "HighProtein",
+  "Influencer"
+]
  2. Addedv1.16.2

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that prices are restaurant-specific, that calorie labels are returned as strings (ranges for build-your-own, single figures for fixed meals), and details the fields returned (dine-in/delivery price, tags, components, images). This is substantial disclosure beyond what a mere 'List meals' would provide, though it does not mention error behavior or pagination, which are not critical for this simple listing tool.

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 multi-sentence but each sentence earns its place. The core action is front-loaded in the first sentence, followed by an expansion of returned content, a pricing distinction, a format nuance, and a source for the parameter. While slightly longer than minimal, it avoids redundancy and is well-structured for comprehension.

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 tool with 2 parameters and no output schema, the description is quite complete. It explains what fields are returned, clarifies the pricing specificity, describes the calorie format, and directs where to obtain the required restaurant_number. There is no pagination or error detail, but for a straightforward list of preset meals, an agent has enough to invoke it correctly and interpret results.

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 description does not add new parameter semantics beyond what the schema already states. The 'meal_type' filter is described in both, and 'restaurant_number' provenance is repeated from the schema description. The description's discussion of calorie labels relates to output, not input, so it does not enhance parameter understanding beyond the schema's baseline.

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 verb (list), the resource (one Chipotle restaurant's preset meals), and the scope (with prices). It also enumerates the meal families (Build-Your-Own, High Protein, Influencer) and explicitly differentiates from the sibling tool chipotle/meals, which lists national definitions without prices, ensuring no ambiguity about what this endpoint offers.

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 states when to use this tool: 'this is the endpoint to use for real pricing rather than /chipotle/meals, which lists the national meal definitions with no prices at all.' It also instructs that restaurant numbers come from GET /chipotle/restaurants, providing a clear prerequisite. This gives the agent both a positive and a negative selection criterion relative to a sibling 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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