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Uber Eats MCP Server

Uber Eats MCP Server

by matiasconcha11
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

uber_eats_switch_address

Switch to a saved delivery address by its label, such as 'home' or 'work', to update your Uber Eats delivery destination for ordering.

Instructions

Switch to a saved delivery address by its label (e.g. 'home', 'work').
Call uber_eats_saved_addresses first to see available addresses.

Args:
    label: The label or name of the saved address to switch to

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": {
      "label": "<label>"
    },
    "name": "uber_eats_switch_address"
  }
}

Input Schema

NameRequiredDescriptionDefault
labelYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention whether the switch affects subsequent operations, whether it requires login, whether there are side effects on cart, or what the return response looks like. For an action tool with zero annotation coverage, more behavioral context is needed.

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 compact with a single purpose line, a usage note, and an Args section. It's efficient and front-loaded. Minor redundancy exists since the Args section restates the label parameter, but overall it's tight and well-organized.

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

Completeness3/5

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

The description covers the prerequisite call and the parameter meaning, which is decent for a single-parameter action tool. There's no output schema content described, though an output schema exists. Given a mutation-type tool with no annotations, it could use more guidance on success/failure behavior and side effects, but the core usage is adequately covered.

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 0%, so the description must compensate for the parameter 'label'. It does clarify 'label' as the label or name of the saved address, which adds some meaning beyond the schema's bare 'Label' title. However, it doesn't specify valid value examples or constraints beyond the indicated e.g. 'home', 'work'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool switches to a saved delivery address by label. The verb 'switch' plus resource 'saved delivery address' gives specific purpose. It distinguishes from siblings like uber_eats_saved_addresses (which lists) and uber_eats_set_address (which likely sets a new/arbitrary address).

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 explicitly instructs calling uber_eats_saved_addresses first to see available addresses, providing valuable usage context and an implicit prerequisite. It doesn't explicitly state when NOT to use it, but the prerequisite guidance effectively orients the agent on correct usage.

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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