How to Use the LA Metro MCP in Vercel AI SDK

Q: Can my Vercel AI SDK UI show both bus and rail data through this LA Metro integration?

Yes. You can query getbusstops and getrailstations in parallel to build a unified transit interface. The Vercel AI SDK handles the incoming streams, allowing you to render both transit types side-by-side.

Q: How does Vercel AI SDK stream live arrival times from the LA Metro endpoints?

The SDK uses the getstoppredictions tool within its streaming text generation loop. This sends real-time arrival estimates directly to the user's browser, bypassing standard loading states.

Q: What is the best way to map a full train journey using these tools?

Use getrailtorail to fetch the optimal path, transfer stations, and travel times. Your client can then present this step-by-step route to the commuter instantly.

Feed live LA Metro transit telemetry directly into your Vercel AI SDK interfaces without making users wait on slow API roundtrips.

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Connect LA Metro MCP to Vercel AI SDK

Create your Vinkius account to connect LA Metro to Vercel AI SDK 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 LA Metro with Vercel AI SDK

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Stream Live Bus Coordinates to Vercel AI SDK

The `get_bus_locations` tool pulls raw GPS coordinates and heading directions from active buses so your Vercel AI SDK app can map them on the fly using this MCP connection. You don't want your users staring at a stale spinner while a bus crawls down Wilshire Boulevard. By calling `get_bus_vehicles` inside your edge functions, you feed live telemetry directly into your UI components. This keeps your interface active and accurate, showing the actual physical position of the fleet instead of relying on outdated schedules.

Predict Rail Arrivals in Your Next.js App

The `get_rail_arrivals` tool fetches real-time train arrivals for specific platforms to prevent commuters from missing their connections at Union Station. Your Vercel AI SDK setup streams these predictions instantly, letting users see exactly when the next Red Line train is pulling in. Combining this with `get_rail_stations` allows your interface to map the entire rail network dynamically. Commuters get immediate, live updates on their screens without manual page refreshes.

Live Service Alerts for Active Commuters

The `get_service_alerts` tool monitors active delays, track maintenance, and sudden route changes across the county transit network. Your Vercel AI SDK frontend can render these alerts as high-priority banners the moment they happen. Using `get_stop_predictions` alongside these alerts ensures your users know why their bus is late before they walk to the stop. It turns raw transit data into direct, live guidance through a dedicated MCP Server.

Setup guide

Set up LA Metro MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all LA Metro tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent LA Metro transactions",
});

console.log(text);
await mcpClient.close();

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 LA Metro. 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.

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Common questions about LA Metro MCP in Vercel AI SDK

You handle GPS lag by checking the predictability indicator returned by `get_bus_locations`. Your Vercel AI SDK application should render a visual warning when the vehicle relies on schedule data rather than active GPS telemetry.

Yes. You can query `get_bus_stops` and `get_rail_stations` in parallel to build a unified transit interface. The Vercel AI SDK handles the incoming streams, allowing you to render both transit types side-by-side.

The SDK uses the `get_stop_predictions` tool within its streaming text generation loop. This sends real-time arrival estimates directly to the user's browser, bypassing standard loading states.

Use `get_rail_to_rail` to fetch the optimal path, transfer stations, and travel times. Your client can then present this step-by-step route to the commuter instantly.

The server only processes public transit telemetry, vehicle IDs, and GPS coordinates. Vinkius runs this MCP Server in an isolated sandbox, keeping your API tokens safe and never touching user location data.

Use it with your favorite AI tools

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