How to Use the MTA MCP in Vercel AI SDK

Q: How do I connect the Vercel AI SDK to the MTA MCP Server?

Install @ai-sdk/mcp and create an HTTP transport client pointing to your endpoint URL. Pass the result of mcpClient.tools() into streamText. Always remember to call mcpClient.close() when the session ends to prevent memory leaks.

Q: Does the MCP server support all subway lines?

Yes, but you must pass the correct feed ID to getsubwayfeed. The feed groups lines together, like ID "1" for the 1/2/3/4/5/6/S lines. Your agent needs to look up the right ID before making the request.

Stream live NYC transit data directly into React frontends using the Vercel AI SDK.

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Connect MTA MCP to Vercel AI SDK

Create your Vinkius account to connect MTA 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 MTA with Vercel AI SDK

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Stream Live Bus and Subway Feeds

The MTA MCP Server connects your Vercel AI SDK frontend directly to the GTFS-RT feeds for buses and subways. You call `get_subway_feed` with a line identifier, and your UI instantly starts rendering train positions. No waiting for a massive JSON payload to resolve. The data streams right into your Next.js components as the agent parses it. Bus tracking requires high-frequency updates. Your agent pulls `get_bus_vehicles` for system-wide coordinates or `get_bus_vehicle_at_stop` for a specific intersection. The user watches the marker move on the map. You bypass static polling entirely.

Build Vercel AI SDK Transit Dashboards

Relying on `get_service_alerts` alongside `get_lirr_feed` gives commuter apps accurate disruption data. Your agent grabs the delay details and the actual train positions simultaneously. You pipe that raw data into a custom React component that displays alternative routes instantly. The same logic applies to Metro-North. Executing `get_metro_north_feed` gives you scheduled versus actual arrival times at Grand Central. Your agent compares those timestamps against `get_system_time` to calculate precise delays, sending the exact wait time to the user's screen.

Map Stations and Stops on the Fly

Firing `get_stations` returns the exact coordinates needed to draw route lines. The MCP server returns the latitude, longitude, and entrance locations for every NYC subway stop. Your agent feeds this into your mapping library before the user even finishes typing their query. Street-level transit is just as precise. Calling `get_bus_routes` followed by `get_bus_stops` maps out the entire M15 or B46 path. When the user clicks a stop, the agent runs `get_bus_estimated_arrival` to show the wait time. It feels instant because the SDK streams the results the millisecond the MTA responds.

Setup guide

Set up MTA 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 MTA 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 MTA transactions",
});

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

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Common questions about MTA MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and create an HTTP transport client pointing to your endpoint URL. Pass the result of `mcpClient.tools()` into `streamText`. Always remember to call `mcpClient.close()` when the session ends to prevent memory leaks.

The SDK itself just executes the tool calls. You need to implement caching in your Next.js route handlers. Relying entirely on real-time polling for `get_subway_feed` will exhaust your quotas fast.

Yes, but you must pass the correct feed ID to `get_subway_feed`. The feed groups lines together, like ID "1" for the 1/2/3/4/5/6/S lines. Your agent needs to look up the right ID before making the request.

Fetching GTFS-RT protobuf data manually requires complex decoding logic. The MTA MCP Server handles the protocol buffers and returns clean JSON. Your agent reads the parsed data immediately.

This MCP server touches geographic coordinates and transit stop IDs. The Vercel AI SDK transmits the query to the endpoint, which fetches the public transit feeds. No user-identifying information or personal device locations are stored on the server.

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