How to Use the Uber MCP in CrewAI

Q: How should CrewAI use Uber MCP Server to manage locations?

A 'Memory Agent' can run getsavedplaces first. This agent pulls all saved spots, which are then passed to a 'Validator Agent' for quick reference during the trip planning cycle.

Q: What if I need an Uber price comparison using CrewAI?

Have one agent run getpriceestimate comparing two general points, and have another agent run getrideestimate for a specific product to provide the final, detailed cost.

Q: Can Uber MCP Server track pickup speed with CrewAI?

Yes. A 'Dispatch Agent' should check gettimeestimate. This gives the crew a crucial metric on how quickly an Uber can arrive at a specified location, helping the user make a timely decision.

Q: Does Uber MCP Server handle profile verification with CrewAI?

You'll use getuserprofile early in your operation. This confirms which account is connected to the session, ensuring that all subsequent actions are correctly attributed to the right user.

Autonomous Uber ride management: Build specialized crews with CrewAI's MCP Server integration.

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Connect Uber MCP to CrewAI

Create your Vinkius account to connect Uber to CrewAI 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 CrewAI

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Plan routes using the Uber MCP Server

You can assign a 'Route Planner Agent' to use `get_place_autocomplete`. This agent researches valid pickup and dropoff locations, returning structured address components for the team. The final step is to have the 'Estimator Agent' run `get_ride_estimate`, using the validated coordinates from the first agent.

Review past trips with the Uber MCP Server

Build a dedicated 'Billing Agent' that runs `get_trip_history`. This allows the crew to pull comprehensive records, including product used, distance, and price for review. The agent can then summarize this data for an end-user report or calculate expenses automatically.

Identify available ride types with the Uber MCP Server

Before any planning starts, assign a 'Product Agent' to use `get_products`. This agent lists all available ride options at a specific location using product IDs and descriptions. This lets your crew decide if the user needs an SUV or just a standard sedan before wasting time on price estimates.

Setup guide

Set up Uber MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Uber tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Uber Analyst",
    goal="Access and analyze Uber data via MCP.",
    backstory="Expert analyst with direct Uber access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Uber transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Uber Analyst",
        goal="Access and analyze Uber data via MCP.",
        backstory="Expert analyst with direct Uber access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Uber transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

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. 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 CrewAI

A 'Memory Agent' can run `get_saved_places` first. This agent pulls all saved spots, which are then passed to a 'Validator Agent' for quick reference during the trip planning cycle.

Have one agent run `get_price_estimate` comparing two general points, and have another agent run `get_ride_estimate` for a specific product to provide the final, detailed cost.

Yes. A 'Dispatch Agent' should check `get_time_estimate`. This gives the crew a crucial metric on how quickly an Uber can arrive at a specified location, helping the user make a timely decision.

You'll use `get_user_profile` early in your operation. This confirms which account is connected to the session, ensuring that all subsequent actions are correctly attributed to the right user.

The server touches location coordinates and trip history details. These include start/end locations, which your autonomous crew must treat as highly sensitive personal information.

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