Does this work for Uber Eats customers or only merchants?

This MCP server is designed exclusively for Uber Eats merchants (restaurant owners). It uses the Uber Eats Marketplace API to manage orders, menus, deliveries, and store operations. Consumer-side features (browsing restaurants, placing orders) require the Uber consumer app. If you're a restaurant owner using Uber Eats for delivery, this is for you.

Can I automatically accept all incoming orders?

Yes! AI agents can monitor pending orders and auto-accept them based on your criteria. You can set up automation rules to accept orders instantly during business hours, reject when kitchen is at capacity, or flag special instructions for manual review. This significantly reduces response times and improves restaurant ratings on the platform.

Does this support multiple restaurant locations?

Absolutely. The API returns all stores associated with your merchant account. Each tool requires a store_id parameter, so you can manage operations independently across all your restaurant locations. AI agents can list all your stores first to get their IDs, then manage menus, orders, and settings for each location separately.

Uber Eats MCP Server for Pydantic AI 14 tools — connect in under 2 minutes

Name: Uber Eats
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Uber Eats through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Uber Eats "
            "(14 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Uber Eats?"
    )
    print(result.data)

asyncio.run(main())

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About Uber Eats MCP Server

What you can do

Connect AI agents to the Uber Eats Marketplace API for complete restaurant and delivery management:

Pydantic AI validates every Uber Eats tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

Monitor incoming orders in real-time with status tracking (PENDING → ACCEPTED → PREPARING → READY → DELIVERED)
Accept or reject orders instantly based on kitchen capacity
Manage restaurant menus — update prices, availability, descriptions, dietary tags
Review order details including customer info, items, special instructions, and totals
Track delivery status with real-time courier GPS location and ETA
Handle order issues including customer complaints and refund requests
View store information and configuration across all registered locations
Mark orders ready for courier pickup when food is prepared

The Uber Eats MCP Server exposes 14 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Uber Eats to Pydantic AI via MCP

Follow these steps to integrate the Uber Eats MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 14 tools from Uber Eats with type-safe schemas

Why Use Pydantic AI with the Uber Eats MCP Server

Pydantic AI provides unique advantages when paired with Uber Eats through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Uber Eats integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Uber Eats connection logic from agent behavior for testable, maintainable code

Uber Eats + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Uber Eats MCP Server delivers measurable value.

Type-safe data pipelines: query Uber Eats with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Uber Eats tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Uber Eats and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Uber Eats responses and write comprehensive agent tests

Uber Eats MCP Tools for Pydantic AI (14)

These 14 tools become available when you connect Uber Eats to Pydantic AI via MCP:

accept_order

This notifies the customer that the restaurant is preparing their food and triggers courier assignment by Uber Eats. Required before marking order as ready for pickup. Use this to acknowledge incoming orders and begin food preparation. Should be done promptly to maintain good restaurant ratings. Accept a pending Uber Eats order to confirm preparation

cancel_order

This is different from rejection - cancellation happens after acceptance and may result in customer dissatisfaction and potential platform penalties. Requires a cancellation reason. Use only when absolutely necessary (kitchen emergency, safety issue, or unavoidable circumstance). Cancel an already accepted Uber Eats order

complete_order

This should be called after confirmation that the delivery was successful. Closes the order lifecycle and triggers final payment processing. Use this to confirm order completion. Mark an order as fully completed (delivered and finalized)

get_delivery_status

Use this to track delivery progress, answer customer inquiries about their order, or coordinate with couriers. Get real-time delivery tracking status for an Uber Eats order

get_menus

Use this to review menu structure, check which items are available/out of stock, or get menu item IDs needed for availability updates. Get complete menu catalog for a specific Uber Eats restaurant

get_order

Use this to review order contents before accepting, verify special instructions, or prepare items correctly. Get complete details of a specific Uber Eats delivery order

get_order_issues

Returns issue descriptions, timestamps, resolution status, and any refunds issued. Use this to review and address order problems, improve quality, and handle disputes proactively. Get reported issues and complaints for a specific Uber Eats order

get_orders

Can filter by status: PENDING (awaiting restaurant acceptance), ACCEPTED (restaurant confirmed), PREPARING (food being prepared), READY (ready for courier pickup), DELIVERED (completed), CANCELLED, or REJECTED. Returns order IDs, customer info, items ordered, totals, special instructions, and timestamps. Use this to monitor order flow, track pending orders requiring action, or review completed deliveries. List all orders for your Uber Eats restaurants with optional status filter

get_store

Use this to review store configuration, verify delivery settings, or check operational status. Get detailed information about a specific Uber Eats restaurant/store

get_stores

Returns external store IDs, names, addresses, operating status, and business details. Use this tool first to get your store IDs, which are required for all other menu and order management operations. List all restaurants/stores associated with your Uber Eats merchant account

mark_order_prep_started

Updates order status to PREPARING and notifies the customer. Use this to keep customers informed about their order progress and provide accurate delivery time estimates. Mark that food preparation has started for an accepted order

mark_order_ready

This triggers courier dispatch notification. Use this when food is complete and waiting for courier arrival. Couriers will be routed to your location for pickup. Mark order as ready for courier pickup (food is packaged and waiting)

reject_order

The customer is notified and refunded automatically. Provide a reason code: "item_unavailable" (key ingredients out of stock), "too_busy" (kitchen at capacity), "kitchen_closed" (outside operating hours), or "other". Use this when unable to fulfill an order. Excessive rejections may affect restaurant visibility on the platform. Reject a pending Uber Eats order when unable to fulfill it

update_menu_item_availability

Set available=true to mark item as in-stock and orderable, or available=false to mark as out-of-stock. Common use: quickly mark items as unavailable when ingredients run out, then re-enable when restocked. Requires external store ID and menu item ID from get_menus result. Toggle availability status of a menu item (mark as in-stock or out-of-stock)

Example Prompts for Uber Eats in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Uber Eats immediately.

"Show me all pending orders and accept them automatically"

"Update the price of 'Margherita Pizza' to R$45.90 and mark it as unavailable"

"Track the delivery status of order #12345 and tell me where the courier is"

Troubleshooting Uber Eats MCP Server with Pydantic AI

Common issues when connecting Uber Eats to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Uber Eats + Pydantic AI FAQ

Common questions about integrating Uber Eats MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Uber Eats MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.