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Uber Eats MCP Server

Uber Eats MCP Server

by matiasconcha11
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

uber_eats_search

Search Uber Eats globally by cuisine, store, or product query to return matching stores and catalog items, enabling quick browsing or direct cart additions.

Instructions

Global Uber Eats search (same flow as the web search bar: getSearchFeedV1, merged
with getFeedV1 for extra catalog rows). Response JSON has:
- stores: list of merchants (name, uuid, url, eta, …)
- items: catalog hits (menu_item_uuid, section_uuid, subsection_uuid, store_uuid)
  for product-style queries like energy drinks or grocery SKUs
- feed_item_types: raw feed module types (debugging)

Use items[] for quick add via menu_item_detail / add_to_cart; use stores[] for browsing.

Args:
    query: Search text (cuisine, store, or product, e.g. 'sushi', 'red bull')

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "uber_eats_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It does disclose the merged feed behavior and describes response JSON structure, which is helpful. However, it doesn't disclose the global scope implications (unlike just nearby), potential rate limits, or whether this requires authentication, though these are somewhat implied by context.

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?

Well-organized with a brief intro, clear bullet-style breakdown of response fields, and an arguments section. Slightly verbose in the introspection of backend endpoints ('getSearchFeedV1, merged with getFeedV1') which may be more detail than an agent needs, but the structure is clear and front-loaded.

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?

Has an output schema that covers return types, and the description adds value by explaining the semantics of each response section (stores vs items vs feed_item_types) and how to use them downstream. It's complete for a search tool—covers what it returns and how to act on the results.

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 0% (query param has title but no descriptions), yet the description provides detailed args documentation: query is 'Search text (cuisine, store, or product, e.g. 'sushi', 'red bull')' with concrete examples. This fully compensates for the schema gap despite being a single parameter.

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 this is a 'Global Uber Eats search' that mirrors the web search bar flow, naming specific endpoints (getSearchFeedV1, getFeedV1) and describing output structure (stores, items, feed_item_types). The purpose is specific and distinct from the many sibling tools like 'nearby_restaurants', 'restaurant_menu', and 'search_history'.

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?

Provides clear guidance: use items[] for quick add via menu_item_detail/add_to_cart, use stores[] for browsing. It states when to use this vs other pathways, though it doesn't explicitly name alternative tools or say when NOT to use it.

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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