MTA MCP Server for Mastra AI 12 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect MTA through the Vinkius and Mastra agents discover all tools automatically — type-safe, streaming-ready, and deployable anywhere Node.js runs.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token — get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"mta": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "MTA Agent",
instructions:
"You help users interact with MTA " +
"using 12 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with MTA?"
);
console.log(result.text);
}
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 MTA MCP Server
Connect your MTA API New York City public transit data platform to any AI agent and take full control of real-time NYC Subway and MTA Bus tracking, arrival predictions, LIRR and Metro-North commuter rail monitoring, and service disruption awareness through natural conversation.
Mastra's agent abstraction provides a clean separation between LLM logic and MTA tool infrastructure. Connect 12 tools through the Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution — deployable to any Node.js host in one command.
What you can do
- Subway Real-Time Feeds — Access live GTFS-RT data for all NYC Subway lines with train positions and arrival predictions
- Bus Routes — List all MTA bus routes across Manhattan, Brooklyn, Queens, Bronx, and Staten Island
- Bus Stops — Get all stops for any bus route with coordinates and sequence information
- Bus Predictions — Get real-time estimated arrival times for any bus stop
- Bus Vehicle Tracking — Track real-time GPS positions of all active MTA bus vehicles
- Service Alerts — Monitor active disruptions across Subway, buses, LIRR, and Metro-North
- Subway Stations — List all 472 NYC Subway stations with coordinates, borough, and entrance data
- LIRR Tracking — Monitor Long Island Rail Road trains with real-time positions and arrivals
- Metro-North Tracking — Track Metro-North Railroad trains serving northern NYC suburbs
- Stop-Level Bus Monitoring — Monitor buses at specific stops with targeted arrival predictions
- Estimated Arrivals — Get route-filtered arrival estimates for buses at any stop
- System Connectivity — Verify API connectivity and synchronize timestamps
The MTA MCP Server exposes 12 tools through the Vinkius. Connect it to Mastra 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 MTA to Mastra AI via MCP
Follow these steps to integrate the MTA MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 12 tools from MTA via MCP
Why Use Mastra AI with the MTA MCP Server
Mastra AI provides unique advantages when paired with MTA through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure — add MTA without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every MTA tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host — Vercel, Railway, Fly.io, or your own infrastructure
MTA + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the MTA MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query MTA, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed MTA as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query MTA on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using MTA tools alongside other MCP servers
MTA MCP Tools for Mastra AI (12)
These 12 tools become available when you connect MTA to Mastra AI via MCP:
get_bus_estimated_arrival
Returns predicted arrival times, route information, destinations, wait times, and delay indicators for each expected bus. Supports both multi-route stop queries and single-route filtered queries. Essential for targeted arrival predictions, route-specific wait time estimation, and passenger trip timing. AI agents should reference this when users ask "when is the next M15 at this stop", "show arrival estimates for route B46 at stop 12345", or need route-filtered arrival data at a specific bus stop. Get estimated arrival times for buses at a stop, optionally filtered by route
get_bus_predictions
Returns predicted arrival times, route IDs, destination information, expected wait times, and whether buses are on schedule or delayed. Based on real-time vehicle tracking and schedule adherence. Essential for real-time bus arrival awareness, passenger waiting time estimation, trip timing, and connection coordination. AI agents should reference this when users ask "when is the next M15 bus at stop 12345", "show predictions for this stop", or need real-time arrival data for a specific bus stop. Stop IDs can be found using get_bus_stops. Get next bus arrival predictions for a specific bus stop
get_bus_routes
Returns route IDs, route names, operators (MTA New York City Bus, MTA Bus Company, private operators under MTA contract), and service area information. Covers local, limited-stop, and Select Bus Service (SBS) routes. Essential for route discovery, service area analysis, transit network understanding, and identifying route IDs for use in stop and prediction queries. AI agents should reference this when users ask "list all bus routes in Manhattan", "what routes serve Brooklyn", or need to identify route IDs for subsequent MTA Bus Time queries. List all MTA bus routes in New York City
get_bus_stops
Returns stop IDs (MonitoringRef), stop names, geographic coordinates (latitude, longitude), stop sequence order, and direction information. Essential for stop discovery, journey planning, accessibility mapping, and identifying stop IDs for use in arrival prediction queries. AI agents should use this when users ask "list all stops on the M15", "find bus stops along Broadway", or need to identify stop IDs for use in get_bus_predictions queries. List all stops for a specific MTA bus route
get_bus_vehicle_at_stop
Returns vehicle IDs, route IDs, current positions, expected arrival times, distances from stop, and operational status. More targeted than system-wide vehicle queries. Essential for stop-level bus tracking, passenger waiting awareness, and real-time arrival estimation at specific stops. AI agents should use this when users ask "what buses are coming to this stop", "track vehicles approaching stop 12345", or need stop-specific bus position data for passenger information. Get buses currently at or approaching a specific bus stop
get_bus_vehicles
Returns vehicle IDs, route affiliations, latitude/longitude coordinates, heading direction, speed, recorded time, and prediction availability. Covers all MTA New York City Bus and MTA Bus Company vehicles in active service. Essential for real-time bus fleet monitoring, passenger arrival estimation, route-level service awareness, and transit operations management. AI agents should use this when users ask "where are all the buses right now", "track bus positions system-wide", or need real-time vehicle position data for fleet visualization. Get real-time positions of all active MTA bus vehicles
get_lirr_feed
Returns train positions, trip updates, scheduled vs. real-time arrivals at stations, delays, track information, and service disruptions across all LIRR branches including Babylon, Ronkonkoma, Hempstead, Port Jefferson, Montauk, and more. Essential for commuter rail tracking, arrival predictions at Penn Station and Grand Central Madison, and LIRR service monitoring. AI agents should reference this when users ask "when is the next LIRR train to Penn Station", "track LIRR train positions", or need real-time commuter rail data for trip planning from Long Island into NYC. Get real-time LIRR train data from the Long Island Rail Road
get_metro_north_feed
Returns train positions, trip updates, scheduled vs. real-time arrivals, delays, track information, and service disruptions across all Metro-North lines including Hudson, Harlem, New Haven, Port Jervis, Pascack Valley, and more. Essential for commuter rail tracking, arrival predictions at Grand Central Madison, and Metro-North service monitoring. AI agents should use this when users ask "when is the next Metro-North train from White Plains", "track Metro-North positions", or need real-time commuter rail data for trip planning from Westchester, Connecticut, or the Hudson Valley into NYC. Get real-time Metro-North Railroad train data
get_service_alerts
Returns alert descriptions, affected lines and stations, severity levels, cause types (maintenance, incident, weather, special events, construction), start and end timestamps, and alternative service recommendations. Essential for service disruption awareness, alternative route planning, passenger communication, and understanding system reliability. AI agents should use this when users ask "are there any delays on the 4/5/6 line", "is LIRR running normally", or need to check service reliability before planning MTA journeys. Get current service alerts and disruptions across the MTA system
get_stations
Returns station IDs, station names, complex IDs (for multi-line stations), borough information (Manhattan, Brooklyn, Queens, Bronx, Staten Island), structure types (underground, elevated, embankment, open cut), latitude/longitude coordinates, and North/East/South/West entrance coordinates. Essential for station discovery, rail network mapping, route planning, and identifying station codes for use in journey planning queries. AI agents should use this when users ask "list all stations in Manhattan", "what is the station code for Times Square", or need to understand the NYC Subway network geography. List all NYC Subway stations with details
get_subway_feed
Supports feed IDs grouped by line: "1" (lines 1,2,3,4,5,6,S), "2" (lines A,C,E), "3" (lines B,D,F,M), "4" (lines G), "5" (lines J,Z), "6" (lines N,Q,R,W), "7" (lines L), "11" (Staten Island Railway), "16" (Shuttle 42nd St), "21" (Shuttle Franklin Ave), "26" (Shuttle Rockaway Park). Returns train positions, trip updates, scheduled vs. real-time arrivals, delays, and service disruptions. Essential for real-time subway tracking, arrival predictions, and service monitoring across the entire NYC Subway system. AI agents should use this when users ask "when is the next 1 train", "show real-time positions for the A line", or need live subway data for trip planning. Feed IDs are required and can be found in MTA documentation. Get real-time subway feed data for specific NYC Subway lines
get_system_time
Returns the official server timestamp in ISO 8601 format. Useful for synchronizing local clocks with the MTA system, verifying API connectivity, testing authentication, and timestamp alignment for real-time data correlation. AI agents should use this as a connectivity check before making more complex queries, or when users need to verify API responsiveness and authentication validity. Get the current MTA Bus Time system timestamp
Example Prompts for MTA in Mastra AI
Ready-to-use prompts you can give your Mastra AI agent to start working with MTA immediately.
"Show me the next trains on the 1/2/3 line."
"When is the next M15 bus arriving at the stop near 14th Street and 3rd Avenue?"
"Check if there are any service alerts affecting the LIRR right now."
Troubleshooting MTA MCP Server with Mastra AI
Common issues when connecting MTA to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpMTA + Mastra AI FAQ
Common questions about integrating MTA MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
Connect MTA with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
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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 MTA to Mastra AI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.