Uber MCP Server for Pydantic AI 9 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Uber through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Uber "
"(9 tools)."
),
)
result = await agent.run(
"What tools are available in Uber?"
)
print(result.data)
asyncio.run(main())
* 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:
Pydantic AI validates every Uber tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- 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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Uber MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from Uber with type-safe schemas
Why Use Pydantic AI with the Uber MCP Server
Pydantic AI provides unique advantages when paired with Uber through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Uber integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Uber connection logic from agent behavior for testable, maintainable code
Uber + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Uber MCP Server delivers measurable value.
Type-safe data pipelines: query Uber with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Uber tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Uber and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Uber responses and write comprehensive agent tests
Uber MCP Tools for Pydantic AI (9)
These 9 tools become available when you connect Uber to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Uber to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiUber + Pydantic AI FAQ
Common questions about integrating Uber MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Uber with your favorite client
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Uber to Pydantic AI
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.