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JavaScript
Remote

ubereats_store

Fetches an UberEats store by ID, returning address, phone, rating, cuisine tags, hours, and full menu with item descriptions and prices. Provides credential-free public store data.

Instructions

Get an UberEats store. Returns a normalized UberEats store: address, phone, rating, cuisine tags, hours tagline, and the full menu (sections with items, descriptions, and prices). Credential-free public UberEats data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
store_idYesUberEats store UUID, as returned by the search endpoint's storeUuid field

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full transparency burden. It adds useful context: 'Credential-free public UberEats data' clarifies access requirements, and 'normalized' indicates data standardization. However, it does not disclose failure modes, potential incompleteness, or any other behavioral traits like rate limits or data freshness.

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 two sentences, front-loaded with the action, and every clause adds value. It succinctly lists the return fields and the access constraint without fluff.

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 low complexity (one parameter, no output schema), the description is quite complete. It enumerates the key return fields and the menu structure, and notes the credential-free nature. Minor gaps like no mention of potential errors or data source caveats exist, but they are not critical for a simple fetch operation.

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?

The schema covers 100% of parameter semantics: store_id is described as 'UberEats store UUID, as returned by the search endpoint's storeUuid field.' The tool description adds no additional parameter-level detail, but the schema already provides sufficient guidance, 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?

The description clearly states the tool's action and resource: 'Get an UberEats store.' It then lists the specific data returned (address, phone, rating, cuisine tags, hours tagline, full menu), which distinguishes it from sibling tools like ubereats_store_menu (menu only) or ubereats_store_reviews (reviews only).

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 context for when to use this tool: when you need a normalized store with comprehensive details including the full menu and basic info. It doesn't explicitly name alternatives or exclusions, but the context implies this is the primary store detail tool, while siblings like ubereats_store_menu or ubereats_store_reviews serve narrower purposes.

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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