How to Use the Lyft MCP in Pydantic AI

Q: Can I use Pydantic AI to parse Lyft ride history?

Yes, you can define a custom model to shape the output of getridehistory. Pydantic AI will automatically validate the dates, costs, and statuses of past trips, throwing an error if any field is missing.

Q: How do I check which ride types are available in my area?

Your agent queries getridetypes using your current latitude and longitude. The tool returns a list of available ride options, capacities, and descriptions for your agent to select from.

Build type-safe transit agents that validate Lyft estimates and bookings at runtime using Pydantic AI.

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Connect Lyft MCP to Pydantic AI

Create your Vinkius account to connect Lyft 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.

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Setup Lyft with Pydantic AI

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Type-Safe Booking Validation with Pydantic AI

Do not let your agent book rides using malformed coordinate data from a faulty `get_cost_estimate` call. Pydantic AI enforces strict runtime validation on every response, ensuring that when you call the estimate tool, the returned pricing structure matches your schema exactly. If the API returns unexpected payload structures, the system raises a validation error immediately. This keeps your agent from passing bad inputs to `request_ride`, avoiding failed bookings or incorrect destinations.

Runtime Verification of Ride Statuses

Tracking active travel requires absolute accuracy when querying `get_ride_details`. Pydantic AI parses the driver's location, vehicle details, and ETA against strict Python type definitions. Such strict parsing prevents your agent from making decisions based on corrupted or partial data. If a network blip returns incomplete ride details, the framework catches it before your agent can mistakenly trigger `cancel_ride`.

Strict Schema Enforcement for Lyft MCP Server Locations

Managing addresses requires structured data, which is why saving locations with `set_location` must be strictly typed. By using this MCP Server with Pydantic AI, your agent can save and retrieve locations using `get_locations` with guaranteed schema conformance. Every location ID, address string, and coordinate pair is checked against your defined Pydantic models. This ensures your agent always has the exact coordinates required to query `get_ride_types` without risking runtime type crashes.

Setup guide

Set up Lyft 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": {
        "lyft-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Lyft MCP in Pydantic AI

It validates the coordinates and ride type IDs before making the API call. If the model attempts to trigger `request_ride` with incomplete data, your Pydantic AI agent uses the MCP schema to halt the execution and prompt the agent to correct the inputs.

Yes, you can define a custom model to shape the output of `get_ride_history`. Pydantic AI will automatically validate the dates, costs, and statuses of past trips, throwing an error if any field is missing.

You should fetch a fresh estimate immediately before booking. Have your Pydantic AI agent call `get_cost_estimate` to validate the pricing, and then pass that validated data directly into the booking tool.

Your agent queries `get_ride_types` using your current latitude and longitude. The tool returns a list of available ride options, capacities, and descriptions for your agent to select from.

Your location IDs and ride history are transmitted over encrypted connections directly to the Lyft API. Vinkius runs this MCP Server ephemerally inside an isolated V8 sandbox that never logs your private travel data.

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