How to Use the Uber MCP in AutoGen

Q: How does Uber use AutoGen for pricing decisions?

AutoGen lets agents debate between getpriceestimate and getrideestimate. They argue about which level of detail is necessary, forcing a comparison that delivers the most accurate cost information before you commit.

Q: Can Uber use AutoGen for trip history review?

Yes. You can assign agents to analyze gettriphistory data. One agent might flag unusually high costs, while another calculates the average distance traveled over time, providing a deeper analysis than simple reporting.

Q: Does AutoGen handle Uber user profile verification?

The agents can confirm the connected account by calling getuserprofile. This initial step allows subsequent negotiations and planning steps to use the correct, verified credentials for all actions.

Run autonomous ride management with AutoGen's multi-agent debate framework.

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Connect Uber MCP to AutoGen

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

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Setup Uber with AutoGen

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Perplexity

Debating the Best Uber Product

Don't just accept the first price. Set up multiple agents to negotiate the best route. One agent calls `get_products` to list options, while another calls `get_ride_estimate`. They debate which combination of product and location yields the optimal result for your needs.

Multi-Agent Trip Planning via MCP Server

Want a foolproof plan? Give agents roles: one handles scheduling (`get_time_estimate`), another handles cost checks (`get_price_estimate`). They communicate until they reach consensus on the best time and price, simulating real-world deliberation.

Reviewing Complex Trip History

The agents can review your past rides using `get_trip_history`. One agent might focus solely on calculating total distance traveled, while another focuses only on the associated costs. They challenge each other's summaries until a comprehensive expense report is generated.

Setup guide

Set up Uber 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 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_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Uber 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_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Uber. 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 MCP in AutoGen

AutoGen lets agents debate between `get_price_estimate` and `get_ride_estimate`. They argue about which level of detail is necessary, forcing a comparison that delivers the most accurate cost information before you commit.

Yes. You can assign agents to analyze `get_trip_history` data. One agent might flag unusually high costs, while another calculates the average distance traveled over time, providing a deeper analysis than simple reporting.

Agents can be programmed to verify your intent against your stored locations. If you mention 'Work,' an agent first checks `get_saved_places` to ensure the correct coordinates are used for the ride request.

It processes location inputs (autocomplete and saved places), financial data from trip history, and product availability details. The process requires constant cross-checking of multiple data points.

The agents can confirm the connected account by calling `get_user_profile`. This initial step allows subsequent negotiations and planning steps to use the correct, verified credentials for all actions.

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