How to Use the Uber Eats MCP in LangChain

Q: How do I handle Uber Eats order status changes using LangChain?

You can monitor the entire flow by calling getorders and filtering by status. When an agent detects a PENDING order, it knows to use acceptorder. This gives you full visibility into whether or not the restaurant accepts the job.

Q: Can I check menu availability for Uber Eats using LangChain?

Absolutely. Run getmenus first; that pulls the complete catalog and available items. If you need to change stock levels, your agent can then use updatemenuitemavailability, requiring both the store ID and item ID.

Q: Does LangChain help with Uber Eats delivery tracking?

Yes. To get real-time updates, you simply call getdeliverystatus. This gives your agent the current location data needed to answer customer questions accurately without manual checks.

Q: What if I need order history for Uber Eats using LangChain?

Use getorderissues to pull all records of problems, timestamps, and refunds associated with a past order. This helps you build reports on quality control.

Q: How does Uber Eats inventory management work with LangChain?

You manage it by first calling getmenus to read what's available. Then, if items run out, your agent can execute updatemenuitemavailability to toggle the stock status immediately.

Build complex, multi-step Uber Eats workflows using LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Uber Eats MCP to LangChain

Create your Vinkius account to connect Uber Eats to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Uber Eats with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Manage Order Lifecycle Chains

Need to track an order from acceptance through delivery? You can chain several steps. For instance, first you call `get_orders` to find the ID, then use that ID in `get_order` to confirm details before calling `accept_order`. This lets your agent handle the full flow. Agents decide which tools run and in what sequence based on intermediate results. If an order is rejected, the chain can automatically call `reject_order` with a reason code like 'item_unavailable' instead of just stopping.

Inventory Management & Status Updates

Run full menu checks and adjust stock instantly. Start by calling `get_menus` to review the current catalog. If ingredients run low, your chain can call `update_menu_item_availability` for specific items. This is much better than just guessing which tools to use. You can also simulate order progress: after receiving an order, you might first call `mark_order_prep_started`, and once packaged, immediately follow up with `mark_order_ready`. The agent handles the state changes automatically.

Handling Operational Complaints

When things go wrong, your chain needs to investigate. Start by calling `get_order_issues` to pull all historical complaints and refund records for a specific order. This data is crucial context. You can also proactively list problems using `get_order_issues` or check the status of any pending orders via `get_orders`. It lets your agent build a comprehensive picture before taking corrective action.

Setup guide

Set up Uber Eats MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Uber Eats tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "uber-eats-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Uber Eats transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Uber Eats. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Uber Eats now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Uber Eats MCP in LangChain

You can monitor the entire flow by calling `get_orders` and filtering by status. When an agent detects a PENDING order, it knows to use `accept_order`. This gives you full visibility into whether or not the restaurant accepts the job.

Absolutely. Run `get_menus` first; that pulls the complete catalog and available items. If you need to change stock levels, your agent can then use `update_menu_item_availability`, requiring both the store ID and item ID.

Yes. To get real-time updates, you simply call `get_delivery_status`. This gives your agent the current location data needed to answer customer questions accurately without manual checks.

Use `get_order_issues` to pull all records of problems, timestamps, and refunds associated with a past order. This helps you build reports on quality control.

You manage it by first calling `get_menus` to read what's available. Then, if items run out, your agent can execute `update_menu_item_availability` to toggle the stock status immediately.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript