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Uber Eats MCP Server

Uber Eats MCP Server

by matiasconcha11
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

uber_eats_restaurant_menu

Fetch a restaurant's full Uber Eats menu using its store URL or slug. Ideal for small eateries; for big grocery stores, search first to avoid giant payloads.

Instructions

Full menu for one store. OK for small restaurants; for supermarkets/groceries
the payload can be enormous (100k+ chars) and exceed the client's tool limit—
prefer uber_eats_search with the product name first, then item-level tools.

Args:
    restaurant_url: Full URL (https://www.ubereats.com/store/...) or restaurant slug

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": {
      "restaurant_url": "<restaurant_url>"
    },
    "name": "uber_eats_restaurant_menu"
  }
}

Input Schema

NameRequiredDescriptionDefault
restaurant_urlYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It proactively discloses a key behavioral trait: the payload can exceed the client's tool limit for large stores (100k+ chars). This is meaningful context an agent needs before invoking. It doesn't mention authentication requirements or rate limits, but the single most consequential behavior—payload size risk—is disclosed.

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?

Very tight and well-structured. The warning and usage direction are front-loaded, and the args detail is efficient. It covers purpose, exclusions, alternative guidance, and parameter semantics in ~45 words. Minor deduction only because the parameter description could arguably be inline in a separate line, but the current structure is near-optimal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no annotations, this is a powerful and complete tool. The one parameter is fully documented, the performance/limit risk is flagged with a practical mitigation path, and there's an output schema available. The complexity here is moderate (single param), but the description fully covers usage caveats and alternatives, leaving nothing critical undocumented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and there's one required param (restaurant_url). The description explains the full semantics: it accepts either the full URL (with example format https://www.ubereats.com/store/...) or a restaurant slug. This adds substantial meaning beyond the schema, which only says 'Restaurant Url'. The description fully compensates for the zero coverage.

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 it fetches the full menu for one store (specific verb: returns menu; specific resource: one store). It distinguishes itself from siblings by explicitly warning against use for supermarkets and directing to uber_eats_search and item-level tools instead, which differentiates it from uber_eats_menu_item_detail and uber_eats_search.

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?

Excellent guidance. It explicitly says when NOT to use (supermarkets/groceries with huge payloads) and directs to alternatives (uber_eats_search first, then item-level tools). This explicit when/when-not with named alternative tools is exemplary and exceeds all sibling definitions.

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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