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JavaScript
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taco_bell_stores

Find Taco Bell restaurants near a latitude and longitude. Returns distance-ordered US stores with addresses, hours, phone, open status, and capability flags.

Instructions

Find Taco Bell restaurants near a location. Returns Taco Bell US restaurants near a latitude/longitude, ordered by distance. Each store carries its store number, full postal address with coordinates, phone, distance, open status, timezone, today's published opening and closing hours, and Taco Bell's own per-store capability flags (breakfast, drive-thru, delivery, mobile ordering, open late, pickup shelves, Live Mas Cafe, online). A coordinate with no Taco Bell nearby returns an empty list rather than an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo0-based page index (default 0)
latitudeYesSearch center latitude
longitudeYesSearch center longitude
page_sizeNoStores per page, 1-50 (default 10)

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 disclosure burden and does so well: it declares geographic scope (US only), ordering (by distance), return richness, and an edge case (empty list rather than error). It does not discuss rate limits or pagination behavior, but the core behavioral traits are disclosed.

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 front-loaded with the main purpose, then provides a compact but valuable summary of return fields. The final sentence about empty-list behavior is important and not redundant. No filler or wasted words.

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?

There is no output schema, so the description compensates by enumerating the returned store fields, checking the main completeness requirement. It also covers the empty-result edge case and required inputs. Minor gaps remain around pagination semantics, but the schema already documents defaults, and the agent can call the tool correctly with the given information.

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%, so parameters are already documented. The description adds context by tying latitude/longitude to 'near a location' and mentioning distance ordering, but it does not add meaningful semantic detail beyond the schema for page or page_size.

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 states a specific action ('Find Taco Bell restaurants near a location'), a resource (US Taco Bell restaurants), and a clear input (latitude/longitude). It also differentiates itself from singular and menu-oriented siblings by emphasizing proximity, distance ordering, and per-store operational details.

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 clearly implies when to use this tool: when you need nearby Taco Bell locations based on coordinates. It does not explicitly name alternatives or exclusion criteria, but the focus on 'near a location' and 'ordered by distance' provides clear selection context among the large sibling set.

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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