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 CrewAI 14 tools — connect in under 2 minutes

Name: Uber Eats
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 14 Tools Framework

Connect your CrewAI agents to Uber Eats through Vinkius, pass the Edge URL in the `mcps` parameter and every Uber Eats tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Uber Eats Specialist",
    goal="Help users interact with Uber Eats effectively",
    backstory=(
        "You are an expert at leveraging Uber Eats tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Uber Eats "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 14 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Uber Eats MCP Server

What you can do

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

When paired with CrewAI, Uber Eats becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Uber Eats tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 14 tools from Uber Eats

Why Use CrewAI with the Uber Eats MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Uber Eats through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Uber Eats + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Uber Eats for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Uber Eats, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Uber Eats tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Uber Eats against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Uber Eats MCP Tools for CrewAI (14)

These 14 tools become available when you connect Uber Eats to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Uber Eats + CrewAI FAQ

Common questions about integrating Uber Eats MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.