How to Use the Uber MCP in Pydantic AI

Q: How does Pydantic AI handle Uber trip history retrieval?

The agent calls gettriphistory and the response payload is validated instantly. You're guaranteed that every field, like distance or price, adheres to the defined schema, preventing silent data corruption.

Q: Can Pydantic AI compare costs for different Uber ride types?

Yes. When using getpriceestimate, Pydantic validates that every returned product type and price structure matches your model. You get reliable, predictable data to make comparisons.

Q: What happens if I need to list available Uber products with Pydantic AI?

The getproducts tool's output is strictly validated. This means the ID, name, and capacity for every ride option returned are exactly what your agent expects them to be.

Q: How does MCP Server manage saved locations for Uber with Pydantic AI?

The getsavedplaces tool's output is type-checked. You can rely on the returned place aliases, addresses, and coordinates being perfectly structured every time you query them.

Get guaranteed structured data for Uber ride management with Pydantic AI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Uber MCP to Pydantic AI

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

GDPR Free for Subscribers

Setup Uber with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Structured Ride Estimates via MCP Server

Your agent gets detailed pricing by calling `get_ride_estimate`. Because the response is validated against a Pydantic model, you'll never get hallucinated fields or unexpected data types. It returns exact pricing for one specific Uber product. This high degree of type safety means your downstream code always knows what to expect, making production agents reliable.

Managing Location Data with MCP Server

You can build location logic using `get_place_autocomplete`. The Pydantic validation ensures that the structured address components returned—like street name and city—always match the expected schema. This is critical for data integrity. It guarantees that even if the underlying API changes slightly, your agent won't crash silently.

Retrieving User Profiles with MCP Server

The `get_user_profile` tool lets your agent verify an Uber account. With Pydantic validation, the returned user data—like names or associated IDs—is guaranteed to match the defined Python types. You'll fail loudly if the API sends garbage data. This reliability is what you need when building critical business logic.

Setup guide

Set up Uber MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "uber-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Uber tools.",
)

result = await agent.run("List recent Uber transactions")
print(result.output)

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Uber now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Uber MCP in Pydantic AI

The agent calls `get_trip_history` and the response payload is validated instantly. You're guaranteed that every field, like distance or price, adheres to the defined schema, preventing silent data corruption.

Yes. When using `get_price_estimate`, Pydantic validates that every returned product type and price structure matches your model. You get reliable, predictable data to make comparisons.

The `get_products` tool's output is strictly validated. This means the ID, name, and capacity for every ride option returned are exactly what your agent expects them to be.

The `get_saved_places` tool's output is type-checked. You can rely on the returned place aliases, addresses, and coordinates being perfectly structured every time you query them.

This MCP Server touches location and user profile details via `get_user_profile` and `get_trip_history`. Using Pydantic ensures that the structure of sensitive records is always correctly handled, minimizing risk.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript