How to Use the LA Metro MCP in Pydantic AI

Q: Can I use Pydantic AI to plan bus-to-rail transfers using LA Metro MCP Server tools?

Yes. Your agent can combine getbusstops and getrailstations to identify nearby transfer points. The type-safe outputs make it easy to chain these tool responses together safely.

Q: Does this setup support offline testing of transit schedules?

You can mock the responses of static tools like getbusschedule and getrailroutes using Pydantic's built-in testing utilities. This lets you verify your agent's routing logic without hitting the live transit API.

Build type-safe Pydantic AI agents that validate real-time LA Metro transit telemetry at runtime to prevent silent failures.

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Setup LA Metro with Pydantic AI

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Type-safe transit tracking with Pydantic AI

Validating live fleet coordinates requires passing payloads from `get_bus_locations` and `get_rail_vehicle_positions` through strict type checks. If the transit API returns malformed GPS coordinates — which happens in tunnels constantly — your system catches it instantly. This strict validation extends to live fleet telemetry. By running `get_bus_vehicles`, your agent processes verified vehicle IDs, heading directions, and predictability indicators without risk of processing corrupt or unexpected payloads.

Guaranteed arrival predictions

Fetching guaranteed wait times requires querying `get_stop_predictions` and `get_rail_arrivals` directly. The framework guarantees that the output structure—including minutes, seconds, and confidence levels—conforms exactly to your defined models. This MCP Server safely pulls data from `get_service_alerts`. Because the alert schemas are strictly validated, your agent can parse alternative service recommendations without hallucinating non-existent routes.

Validated rail journey planning

Calculating transfer paths requires calling `get_rail_to_rail` and `get_rail_stations` to prevent routing errors. Pydantic AI ensures that station codes and transfer sequences are typed correctly throughout the routing pipeline. You can also query `get_bus_schedule` and `get_bus_stops` to build multi-modal routing engines. The framework validates every schedule sequence and geographic coordinate, ensuring your agent never recommends a physically impossible connection.

Setup guide

Set up LA Metro 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": {
        "la-metro-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about LA Metro MCP in Pydantic AI

Create an MCPToolset instance pointing to your server's HTTP or SSE endpoint. Pass this toolset into the toolsets list of your Agent constructor to instantly expose all twelve transit validation tools.

The framework will raise a validation error immediately. This prevents your agent from making decisions based on corrupted transit data, allowing you to handle the error gracefully in your application code.

Yes. Your agent can combine `get_bus_stops` and `get_rail_stations` to identify nearby transfer points. The type-safe outputs make it easy to chain these tool responses together safely.

You can mock the responses of static tools like `get_bus_schedule` and `get_rail_routes` using Pydantic's built-in testing utilities. This lets you verify your agent's routing logic without hitting the live transit API.

The validation layer operates entirely within your local application memory. The server only handles public transit schedules and vehicle locations, meaning no personal commuter telemetry or private search data is ever logged or stored.

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