MTA MCP Server for Claude Desktop 12 tools — connect in under 2 minutes
Claude Desktop is Anthropic's native application for interacting with Claude AI models on macOS and Windows. It was the first consumer application to ship with built-in MCP support, making it the reference implementation for the Model Context Protocol standard.
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Vinkius supports streamable HTTP and SSE.
Vinkius Desktop App
The modern way to manage MCP Servers — no config files, no terminal commands. Install MTA and 2,500+ MCP Servers from a single visual interface.
{
"mcpServers": {
"mta": {
// Your Vinkius token. get it at cloud.vinkius.com
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
* 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.
Claude Desktop is the definitive way to connect MTA to your AI workflow. Add Vinkius Edge URL to your config, restart the app, and Claude immediately exposes all 12 tools in the chat interface. ask a question, Claude calls the right tool, and you see the answer. Zero code, zero context switching.
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 Claude Desktop 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 Claude Desktop via MCP
Follow these steps to integrate the MTA MCP Server with Claude Desktop.
Open Claude Desktop Settings
Go to Settings → Developer → Edit Config to open claude_desktop_config.json
Add the MCP Server
Paste the configuration above into the mcpServers section
Restart Claude Desktop
Close and reopen Claude Desktop to load the new server
Start using MTA
Look for the 🔌 icon in the chat. your 12 tools are now available
Why Use Claude Desktop with the MTA MCP Server
Claude Desktop by Anthropic provides unique advantages when paired with MTA through the Model Context Protocol.
Claude Desktop is the reference MCP client. it was designed alongside the protocol itself, ensuring the most complete and stable MCP implementation available
Zero-code configuration: add a server URL to a JSON file and Claude instantly discovers and exposes all available tools in the chat interface
Claude's extended thinking capability lets it reason through multi-step tool usage, chaining multiple API calls to answer complex questions
Enterprise-grade security with local config storage. your tokens never leave your machine, and connections go directly to Vinkius Edge network
MTA + Claude Desktop Use Cases
Practical scenarios where Claude Desktop combined with the MTA MCP Server delivers measurable value.
Interactive data exploration: ask Claude to query DNS records, look up WHOIS data, and cross-reference results in a single conversation
Ad-hoc security audits: type a domain name and let Claude enumerate subdomains, check DNS history, and flag configuration anomalies. all through natural language
Executive briefings: generate comprehensive domain intelligence reports by asking Claude to compile findings into a formatted summary
Learning and training: new team members can explore API capabilities conversationally without needing to read documentation
MTA MCP Tools for Claude Desktop (12)
These 12 tools become available when you connect MTA to Claude Desktop 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 Claude Desktop
Ready-to-use prompts you can give your Claude Desktop 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 Claude Desktop
Common issues when connecting MTA to Claude Desktop through the Vinkius, and how to resolve them.
Server not appearing after restart
~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\\Claude\\ (Windows).Authentication error
Tools not showing in chat
MTA + Claude Desktop FAQ
Common questions about integrating MTA MCP Server with Claude Desktop.
How does Claude Desktop discover MCP tools?
claude_desktop_config.json file and connects to each configured MCP server. It calls the tools/list endpoint to fetch the schema for every available tool, then surfaces them as clickable options in the chat interface via the 🔌 icon.What happens if the MCP server is temporarily unavailable?
Can I connect multiple MCP servers simultaneously?
mcpServers section of the config file. Each server appears as a separate tool provider, and Claude can use tools from multiple servers in a single conversation turn.Is there a limit on the number of tools per server?
Does Claude Desktop support Streamable HTTP transport?
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TypeScript-native agent framework for modern web stacks.
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect MTA to Claude Desktop
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.