Can this MCP server book rides automatically?

This MCP server provides price estimates, time estimates, product availability, and trip history. Direct ride booking requires additional Uber API permissions (Requests API) available only to approved enterprise partners. Use this server for planning and comparison workflows.

What Uber API permissions do I need?

You need a Server Token from the Uber Developer Portal with access to: Products, Price Estimates, Time Estimates, History, User Profile, and Places endpoints. These are available in the standard developer tier without special approval.

Does this work with Uber Eats deliveries?

This MCP server focuses on Uber ride-sharing products (UberX, Black, Comfort, etc.). For Uber Eats merchant/delivery operations, a separate integration would be needed. Contact Uber for Eats API access.

Uber MCP Server for CrewAI 9 tools — connect in under 2 minutes

Name: Uber
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to Uber through Vinkius, pass the Edge URL in the `mcps` parameter and every Uber 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 Specialist",
    goal="Help users interact with Uber effectively",
    backstory=(
        "You are an expert at leveraging Uber 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 "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 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 MCP Server

What you can do

Connect your AI agents to the Uber platform for seamless ride management and trip planning:

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

Get available ride products (UberX, Black, Comfort) at any location
Estimate prices across all ride types before booking
Compare pickup times to choose the fastest option
View complete trip history with pricing and route data
Save and manage favorite places (Home, Work, custom locations)
Autocomplete place searches for accurate pickup/dropoff coordinates

The Uber MCP Server exposes 9 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 to CrewAI via MCP

Follow these steps to integrate the Uber 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 9 tools from Uber

Why Use CrewAI with the Uber MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Uber 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 + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Uber 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, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Uber 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 against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Uber MCP Tools for CrewAI (9)

These 9 tools become available when you connect Uber to CrewAI via MCP:

add_saved_place

Requires alias name, latitude, and longitude. Optionally include a full address string. The alias can be home, work, or any custom string. Returns the saved place details. Save a new place for the authenticated Uber user

get_place_autocomplete

Requires current user location to bias results. Returns place descriptions and structured address components. Use this to help users select valid pickup/dropoff locations before requesting rides. Autocomplete place predictions for Uber locations

get_price_estimate

Prices are in local currency. Use this to compare costs across different Uber ride types before booking. Get price estimate for an Uber ride between two locations

get_products

) available at the specified latitude/longitude. Returns product IDs, display names, capacity, and descriptions. Use this to see which ride options are available before requesting a ride or price estimate. Get available Uber products at a location

get_ride_estimate

More specific than price estimates as it targets one product. Use this to get exact pricing before requesting a ride. Get detailed ride estimate for a specific Uber product

get_saved_places

Returns place aliases, addresses, and coordinates. Use this to quickly reference saved locations for ride requests or price estimates without typing addresses. List saved places for the authenticated Uber user

get_time_estimate

Use this to compare how quickly different Uber services can pick you up. Lower times mean faster pickups. Get estimated pickup time for Uber at a location

get_trip_history

Returns trip date, start/end locations, product used, distance, and price. Use this to review past rides, calculate expenses, or find a previous trip details. Get trip history for the authenticated Uber user

get_user_profile

Use this to verify authentication and confirm which Uber account is connected. Get the authenticated Uber user profile

Example Prompts for Uber in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Uber immediately.

"Estimate the price for an UberX from my home to the airport at 3pm tomorrow"

"Show me my last 10 Uber trips with total spending"

"What Uber products are available at my current location and how fast can they pick me up?"

Troubleshooting Uber MCP Server with CrewAI

Common issues when connecting Uber 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 + CrewAI FAQ

Common questions about integrating Uber 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.