How to Use the Uber Eats MCP in AutoGen

Q: Can AutoGen handle Uber Eats menu changes?

Yes. You can assign one agent to check current stock via getmenus, and another to execute necessary updates using updatemenuitemavailability. The debate ensures the change is logged correctly.

Q: Does AutoGen help with Uber Eats order acceptance?

Absolutely. An agent can first call getorders to pull pending jobs. Before calling acceptorder, it can run a validation check via another tool, ensuring the restaurant is actually ready.

Q: What if I need historical Uber Eats data with AutoGen?

You use getorderissues to gather all complaint reports. The agents then discuss these issues—one summarizing the complaints, and another proposing a policy change based on that consensus.

Develop consensus-driven Uber Eats operational systems with AutoGen.

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Setup Uber Eats with AutoGen

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Multi-Agent Order Flow Negotiation

This server lets agents debate order status changes. Set up a 'Kitchen Agent' and a 'Logistics Agent.' When an order arrives, the Kitchen agent can call `accept_order`, but it must confirm preparation readiness using `mark_order_prep_started`. The Logistics agent then uses `get_delivery_status` to coordinate pickup. The system requires consensus: one agent might try to mark it ready (`mark_order_ready`), while another flags a conflict, forcing the user to review the order first via `get_order`.

Conflict Resolution & Dispute Handling

AutoGen is perfect for complex problem-solving. If an agent detects discrepancies using `get_order_issues`, it can initiate a dispute resolution dialogue. One agent might pull the complaint data, while another drafts the required response based on policy. This simulates a team reviewing the situation: one agent reads the report from `get_order_issues`; another consults the order details from `get_order` to form a final recommendation.

Inventory and Operational Decision Making

You need agents to manage stock levels autonomously. The 'Inventory Agent' checks `get_menus`. If an item is flagged as out of stock, it automatically calls `update_menu_item_availability` for the specific ID. This prevents order acceptance. A second agent can check store readiness via `get_store`, ensuring all operational parameters are met before accepting new business.

Setup guide

Set up Uber Eats MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Uber Eats tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Uber Eats_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Uber Eats data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Uber Eats_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Uber Eats data")
    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.

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Common questions about Uber Eats MCP in AutoGen

AutoGen allows agents to debate the necessity of a cancellation. If an agent calls `cancel_order`, another agent might challenge it, forcing a review using `get_order` details before the final action is taken.

Yes. You can assign one agent to check current stock via `get_menus`, and another to execute necessary updates using `update_menu_item_availability`. The debate ensures the change is logged correctly.

You give one agent the task of monitoring status. It continuously calls `get_delivery_status`, and if issues arise, a secondary 'Support Agent' takes over to analyze potential problems using other tools.

Absolutely. An agent can first call `get_orders` to pull pending jobs. Before calling `accept_order`, it can run a validation check via another tool, ensuring the restaurant is actually ready.

You use `get_order_issues` to gather all complaint reports. The agents then discuss these issues—one summarizing the complaints, and another proposing a policy change based on that consensus.

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