Can my AI check when the next subway train is arriving at my station?

Yes! Use the `get_subway_feed` tool with the appropriate feed ID for your line. Feed IDs are grouped: "1" covers lines 1,2,3,4,5,6,S; "2" covers A,C,E; "3" covers B,D,F,M; "4" is G; "5" is J,Z; "6" covers N,Q,R,W; "7" is L; "11" is Staten Island Railway. This returns real-time GTFS-RT data with train positions, trip updates, scheduled vs. real-time arrivals, and delay information. For station-level predictions, combine with `get_stations` to find your station code first.

How do I check when the next MTA bus is arriving at a specific stop?

First use `get_bus_stops` with a route ID to find the stop ID (MonitoringRef) for your location. Then use `get_bus_predictions` with that stop ID to get real-time estimated arrival times, route information, destinations, and delay indicators. For more targeted predictions, use `get_bus_estimated_arrival` which allows filtering by both stop ID and route ID. Stop IDs are numeric identifiers assigned by MTA to each physical bus stop across NYC.

MTA MCP Server for AutoGen 12 tools — connect in under 2 minutes

Name: MTA
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 12 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add MTA as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="mta_agent",
            tools=tools,
            system_message=(
                "You help users with MTA. "
                "12 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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* 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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use MTA tools. Connect 12 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

Follow these steps to integrate the MTA MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 12 tools from MTA automatically

Why Use AutoGen with the MTA MCP Server

AutoGen provides unique advantages when paired with MTA through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use MTA tools to solve complex tasks

Role-based architecture lets you assign MTA tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive MTA tool calls

Code execution sandbox: AutoGen agents can write and run code that processes MTA tool responses in an isolated environment

MTA + AutoGen Use Cases

Practical scenarios where AutoGen combined with the MTA MCP Server delivers measurable value.

Collaborative analysis: one agent queries MTA while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from MTA, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using MTA data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process MTA responses in a sandboxed execution environment

MTA MCP Tools for AutoGen (12)

These 12 tools become available when you connect MTA to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

Common issues when connecting MTA to AutoGen through the Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

MTA + AutoGen FAQ

Common questions about integrating MTA MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call MTA tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.