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Uber MCP. Compare fares and plan routes using specific location data.

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Works with every AI agent you already use

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Uber MCP on Cursor AI Code Editor MCP Client Uber MCP on Claude Desktop App MCP Integration Uber MCP on OpenAI Agents SDK MCP Compatible Uber MCP on Visual Studio Code MCP Extension Client Uber MCP on GitHub Copilot AI Agent MCP Integration Uber MCP on Google Gemini AI MCP Integration Uber MCP on Lovable AI Development MCP Client Uber MCP on Mistral AI Agents MCP Compatible Uber MCP on Amazon AWS Bedrock MCP Support

Just plug in your AI agents and start using Vinkius.

Uber MCP Server handles entire ride logistics through your agent. It lets you check price estimates across multiple products, find available services at any location, and pull detailed trip history for expense reporting.

You use it to plan multi-stop routes without opening the Uber app—it just runs the necessary tools.

What your AI agents can do

Add saved place

Adds a new custom, home, or work location to your profile using coordinates and an alias name.

Get place autocomplete

Predicts valid addresses and provides structured components based on the user's current location input.

Get price estimate

Compares potential costs across all ride types for a given start and end point.

+ 6 more capabilities included
Estimate Fare Costs

The agent compares the projected price across various Uber ride types (e.g., UberX, Comfort) between two points.

Check Availability and Time

It determines which specific ride products are available at your current location and how quickly a car can pick you up.

Manage Saved Locations

You save custom or common addresses (like 'Gym' or 'Office B') so the agent always knows where to go without needing full street addresses.

Retrieve Trip History

The server pulls a list of past rides, providing dates, start/end locations, and total spending for expense review.

Autocomplete Addresses

It takes your input—like 'Starbucks near downtown'—and returns structured coordinates ready for use in pricing or trip calls.

Supported MCP Clients

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients
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AI Agent

Uber MCP Server: 9 Tools for Logistics and Pricing

These nine tools let your agent manage every part of a ride plan—from finding available cars to logging historical expenses.

add019d7617

add saved place

Adds a new custom, home, or work location to your profile using coordinates and an alias name.

get019d7617

get place autocomplete

Predicts valid addresses and provides structured components based on the user's current location input.

get019d7617

get price estimate

Compares potential costs across all ride types for a given start and end point.

get019d7617

get products

Lists all available Uber services (e.g., Comfort, Black) at a specific location coordinate.

get019d7617

get ride estimate

Calculates the detailed price for one specific ride product type between two locations.

get019d7617

get saved places

Retrieves a list of all custom and predefined addresses you have saved in your Uber account.

get019d7617

get time estimate

Determines the estimated wait time for a ride product at a specific location coordinate.

get019d7617

get trip history

Pulls all past trip data, including dates, routes, products used, and total costs.

get019d7617

get user profile

Confirms which Uber account is connected to the server for authentication purposes.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
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Start building

Make Your AI Do More

Start with Uber, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 4,700+ others, all in one place
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  • Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector

This MCP Server gives your agent control over entire ride logistics, letting you handle planning and expense tracking without opening the actual Uber app. You use it to check costs, find available services at any spot, or pull detailed trip history for accounting purposes.

Getting Setup & Defining Locations:
You first confirm which account we're talking about by calling get_user_profile, which authenticates your connected Uber account details. If you don't want to type out an address every time—say, your office or the gym—you can use add_saved_place; just give it coordinates and a name like 'Work' or 'Gym', and it saves it for later.

You check what addresses you already have saved by running get_saved_places, which pulls a list of all those custom or predefined locations. When you only know something general, like 'Starbucks near downtown,' use get_place_autocomplete. This tool takes your raw text input and spits out structured coordinates and valid address components ready for any calculation.

Estimating Costs & Planning the Trip:
When you're figuring out a route, we don't guess. To see potential costs across multiple ride types—like comparing an UberX to a Comfort ride—you call get_price_estimate, just supplying the start and end points. If you already know exactly what product type you want, use get_ride_estimate; this calculates the specific price for that single product between two locations.

To see which services are actually available at your current spot—say, if they've pulled all the Black cars away—you run get_products, and it lists every service (like Comfort or Black) tied to a coordinate. You can also check how long you gotta wait for a car by using get_time_estimate; give it the location and product type, and it tells you the estimated pickup time.

Tracking History & Expenses:
Need to file your expenses? Don't dig through emails or receipts. You run get_trip_history, and it pulls a complete list of all your past rides. This data includes dates, where you went from and to, what product you used for the trip, and the total money spent—perfect for expense review.

The server handles everything from location setup to cost comparison and historical record pulling, letting your agent manage complex travel itineraries without you needing to touch a booking app.

How Uber MCP Works

  1. 1 First, you connect your Uber account via a Server Token. This confirms the agent has access to your ride data and saved locations.
  2. 2 Next, you tell your AI client what you want—for instance, 'What's the fastest way from my office to the airport?'
  3. 3 The server runs multiple tools (get_products, get_time_estimate) and sends back a structured answer that includes pricing comparisons and estimated pickup times.

The bottom line is: your agent handles all the multi-step planning—checking prices, availability, and time estimates—in one conversation thread.

Who Is Uber MCP For?

This server is for corporate assistants, operations managers, and field logistics coordinators. If you spend more than a few hours a week manually logging travel expenses or coordinating ad-hoc business trips, this saves the clicks. You need it when planning isn't simple; you need comparison data.

Executive Assistant

Scheduling multi-day trips and compiling expense reports by running get_trip_history and comparing costs using get_price_estimate.

Logistics Coordinator

Running availability checks (get_products) across different zones or locations to ensure the fastest pick-up time is always chosen.

Project Manager

Modeling potential travel costs for team deployments by calculating estimated fares between multiple required meeting sites using get_place_autocomplete and get_price_estimate.

What Changes When You Connect

  • Expense reporting gets faster. Instead of manually logging receipts, use get_trip_history to pull a complete list of past rides for accounting review. You get the dates, locations, and costs instantly.
  • No more guessing on cost. By running get_price_estimate, you can compare multiple ride types—UberX vs. Comfort—before confirming your travel plans, making budgeting immediate.
  • Location planning is precise. Use get_place_autocomplete first to lock in accurate coordinates for pickup and dropoff points, eliminating manual address entry errors.
  • Time savings on coordination. When you need the fastest option, running get_time_estimate shows the predicted wait time across different services, letting your agent choose optimally.
  • Workflow automation. Your AI client can use add_saved_place to store common meeting spots (like 'Client HQ') so every subsequent request is faster and more reliable.

Real-World Use Cases

01

Modeling a Multi-City Business Trip

A project manager needs to estimate the total cost of three separate meetings. They ask their agent to use get_place_autocomplete for all three addresses, then run get_price_estimate between each pair. The server returns a total budget range, saving hours of manual cross-referencing.

02

Handling Quarterly Expense Reports

The executive assistant needs to submit an expense report for the last quarter. Instead of hunting through emails and receipts, they ask their agent to call get_trip_history. The server immediately pulls all relevant trip data, ready for PDF export.

03

Last-Minute Ride Comparison

You're at a new location and need to know the best ride. You ask your agent what products are available (get_products) and then compare their estimated wait times using get_time_estimate. The system instantly guides you to the fastest, most suitable option.

04

Setting up Recurring Sites

A field worker needs a reliable address for a new client site. They use get_place_autocomplete first, then tell their agent to save it using add_saved_place. Next time, they just refer to the saved alias instead of typing coordinates.

The Tradeoffs

Calling estimates without locations

Telling your agent: 'Estimate me a ride price.' The server fails because it needs specific start and end points to run get_price_estimate.

Always use get_place_autocomplete first with the general area, then pass those confirmed coordinates (lat/long) into both get_price_estimate and other location tools.

Forgetting available services

Asking for a 'luxury ride' when only standard service is available. The agent tries to call get_ride_estimate but fails or returns incorrect pricing.

First, check what services are even possible at your current spot by calling get_products. Only then should you use get_ride_estimate for a specific product.

Mismanaging saved spots

Typing out 'Home Address' every time, wasting tokens and slowing down the conversation flow.

Use add_saved_place once to establish your key locations. Then, refer to them by alias in any future request or estimate.

When It Fits, When It Doesn't

Use this server if your primary need is comparing costs and planning routes based on known coordinates and service types. Specifically, you must be able to compare multiple pricing options (get_price_estimate) or pull structured historical data for accounting (get_trip_history).

Don't use it if all you want is a single-point booking without any comparison logic; the server gives you data, not just a ticket. Also, don't rely on general mapping services—this tool requires specific coordinates and uses get_place_autocomplete to get them right.

It’s ideal when your workflow involves: 1) Discovery (using get_place_autocomplete); 2) Selection/Planning (comparing via get_products, get_time_estimate, or get_price_estimate); and 3) Record Keeping (get_trip_history).

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.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

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Sandboxed per request

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No stored credentials

DLP Enforced

Policy on every call

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EU data residency

Token Compression

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How we secure it →

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This server provides 9 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.

Available Capabilities

add_saved_place get_place_autocomplete get_price_estimate get_products get_ride_estimate get_saved_places get_time_estimate get_trip_history get_user_profile

Dealing with travel planning usually means jumping between three different tabs.

Today, planning a trip requires manual hops: You check Google Maps for coordinates; you open the Uber site to compare prices across UberX and Comfort; then you switch to your expense tracking dashboard to log the details. It’s slow, prone to copy-paste errors, and forces context switching.

With this MCP server, you just talk to your agent. You say, 'Plan a trip from my work to the client.' The system automatically calls `get_place_autocomplete` for precision, runs `get_products` to see options, and returns all necessary pricing data—all in one response.

The Uber MCP Server: Get detailed ride estimates and history.

Before this server, checking a price meant guessing or doing multiple web searches. You couldn't easily compare the cost of three different car types simultaneously, nor could you get historical data without logging into separate portals.

Now, your agent runs `get_price_estimate` and presents an immediate breakdown: 'UberX is $25; Comfort is $38.' The comparison happens in natural language. It's that simple.

Common Questions About Uber MCP

How do I check costs for multiple rides using get_price_estimate? +

You pass the same start and end coordinates to get_price_estimate. The server compares the fares across all available Uber products in one go, giving you a full cost breakdown.

What is the difference between get_products and get_ride_estimate? +

get_products lists every type of service (UberX, Black) available at a location. get_ride_estimate takes that knowledge and gives you the specific price for one selected product.

Can I use get_trip_history to file expense reports? +

Yes. The history tool retrieves full trip records, including dates, start/end coordinates, distance, and final cost. This data is structured for easy aggregation into expense sheets.

How do I make sure my saved locations are correct using add_saved_place? +

You must provide the alias name (e.g., 'Gym') along with accurate latitude and longitude coordinates to add_saved_place. It saves these structured details for future use.

How do I verify my Uber connection status using get_user_profile? +

Run get_user_profile. This confirms that your AI agent is connected to the correct, active Uber account. It’s a quick way to validate credentials and ensure data accuracy before running any other command.

What if I don't know the exact coordinates? Can get_place_autocomplete help me find valid pickup/dropoff points? +

Yes, use get_place_autocomplete. This tool predicts and structures addresses based on your current location. It gives you full place descriptions and structured address components, making it easy to select a precise spot for the ride request.

How do I find out which service will pick me up fastest using get_time_estimate? +

Run get_time_estimate. This provides estimated pickup times for various services at a location. Comparing these numbers directly helps you choose the option that minimizes wait time, regardless of cost.

How do I list all my stored locations using get_saved_places? +

Call get_saved_places. This retrieves a full list of your saved place aliases, addresses, and coordinates. You don't have to type out known destinations; the AI agent pulls them directly from your account.

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.

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Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
+ other MCP clients

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