Lyft 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 Lyft 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 Lyft "
"(9 tools)."
),
)
result = await agent.run(
"What tools are available in Lyft?"
)
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 Lyft MCP Server
What you can do
Connect AI agents to the Lyft platform for complete ride automation:
Pydantic AI validates every Lyft 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 types (Lyft, XL, Lux) at any location
- Estimate ride costs across all products before booking
- Compare pickup ETAs to choose the fastest option
- Request rides directly with origin and destination coordinates
- Track active rides with driver info, vehicle details, and real-time status
- Cancel rides when plans change
- View complete ride history with pricing and route data
- Save favorite locations (Home, Work, custom places)
The Lyft 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 Lyft to Pydantic AI via MCP
Follow these steps to integrate the Lyft 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 Lyft with type-safe schemas
Why Use Pydantic AI with the Lyft MCP Server
Pydantic AI provides unique advantages when paired with Lyft 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 Lyft integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Lyft connection logic from agent behavior for testable, maintainable code
Lyft + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Lyft MCP Server delivers measurable value.
Type-safe data pipelines: query Lyft with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Lyft tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Lyft and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Lyft responses and write comprehensive agent tests
Lyft MCP Tools for Pydantic AI (9)
These 9 tools become available when you connect Lyft to Pydantic AI via MCP:
cancel_ride
Cancellation policies vary based on ride status - cancellations after driver assignment may incur fees. Use this to cancel rides that were booked by mistake or are no longer needed. Cancel an existing Lyft ride request
get_cost_estimate
Prices are in local currency (USD). Use this to compare costs across different Lyft products before booking. Get cost estimate for a Lyft ride between two locations
get_eta_estimate
Use this to compare how quickly different Lyft services can reach you. Lower minutes mean faster pickups. Get estimated arrival times for Lyft at a location
get_locations
Returns location IDs, names, addresses, and coordinates. Use this to quickly reference saved locations for ride requests without typing full addresses. Get saved locations for the Lyft account
get_ride_details
Use this to track your active ride or review past ride details. Get details of a specific Lyft ride
get_ride_history
Returns ride date, status, origin/destination, ride type, driver, and cost. Use this to review past rides, calculate expenses, or find previous trip details. Get ride history for the authenticated Lyft account
get_ride_types
) available at the specified latitude/longitude. Returns ride type IDs, display names, capacity, and descriptions. Use this to see which ride options are available before requesting price or time estimates. Get available Lyft ride types at a location
request_ride
Requires ride type ID (from get_ride_types), origin coordinates, and destination coordinates. Optionally include pickup/dropoff addresses for clarity. Returns the ride ID and status. Use this to book a ride after confirming price and availability. Request a new Lyft ride
set_location
Requires location ID, latitude, and longitude. Optionally include a display name. The location ID can be home, work, or any custom string. Returns the saved location details. Use this to manage your favorite pickup/dropoff spots. Save or update a location for the Lyft account
Example Prompts for Lyft in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Lyft immediately.
"Get me a price estimate from JFK Airport to Times Square for a Lyft XL"
"Book me a Lyft from my home to San Francisco International Airport"
"Show me my last 20 Lyft rides and total spending"
Troubleshooting Lyft MCP Server with Pydantic AI
Common issues when connecting Lyft to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiLyft + Pydantic AI FAQ
Common questions about integrating Lyft 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 Lyft with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
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.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
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 Lyft to Pydantic AI
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.