4,000+ servers built on MCP Fusion
Vinkius
LangChainFramework
Why use WMATA MCP Server with LangChain?

Bring Public Transit
to LangChain

Create your Vinkius account to connect WMATA to LangChain and start using all 12 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.

MCP Inspector GDPR Free for Subscribers
Get Bus IncidentsGet Bus PositionsGet Bus Route DetailsGet Bus RoutesGet Circuit PredictionsGet Elevator IncidentsGet Next RailGet Parking LotsGet Rail IncidentsGet Rail StationsGet Station EntrancesGet Station Prediction
ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

ClaudeClaude
ChatGPTChatGPT
CursorCursor
GeminiGemini
WindsurfWindsurf
VS CodeVS Code
JetBrainsJetBrains
VercelVercel
+ other MCP clients
WMATA

What is the WMATA MCP Server?

Connect your WMATA API Washington DC public transit data platform to any AI agent and take full control of real-time Metrorail and Metrobus tracking, incident monitoring, and station information through natural conversation.

What you can do

  • Next Rail Predictions — Get real-time next train predictions system-wide or at specific Metrorail stations
  • Station Discovery — List all Metrorail stations with codes, addresses, coordinates, and line affiliations
  • Station Predictions — Get detailed next train arrivals at any specific Metrorail station
  • Metrobus Tracking — Track real-time GPS positions of all Metrobus vehicles or filter by route
  • Bus Route Details — Get complete route information including stop sequences for any Metrobus route
  • Rail Incidents — Monitor active service disruptions affecting Metrorail lines and stations
  • Bus Incidents — Check current incidents and detours affecting Metrobus service
  • Elevator Outages — Track elevator and escalator outages for accessibility planning
  • Station Entrances — Get street-level entrance information for any Metrorail station
  • Parking Lots — Find station parking availability, fees, and amenities for park-and-ride planning
  • Bus Routes — Browse all Metrobus routes operating across DC, Maryland, and Virginia
  • Bus Predictions — Get next bus arrival predictions at stations and stops

How it works

  1. Subscribe to this server
  2. Enter your WMATA API key (free from the Developer Portal)
  3. Start tracking DC Metro transit from Claude, Cursor, or any MCP-compatible client

No more navigating the Metro website or manually checking train times. Your AI acts as a dedicated DC transit analyst and trip planning assistant.

Who is this for?

  • DC Commuters — track trains and buses, check incidents, and plan park-and-ride trips
  • Tourists — navigate the Metrorail system with station discovery and entrance guidance
  • Accessibility Planners — verify elevator status and accessible entrances before traveling
  • Transit Apps — integrate real-time WMATA data into mobility and journey planning applications

Built-in capabilities (12)

get_bus_incidents

Returns incident descriptions, affected route IDs, detour information, bus stop closures, incident types (accident, road closure, construction, mechanical), start timestamps, and alternative service recommendations. Essential for bus service disruption awareness, alternative route planning, and passenger communication. AI agents should use this when users ask "are there any bus delays", "is route 30N running normally", or need to check Metrobus service reliability. Get current incidents affecting Metrobus service

get_bus_positions

Returns bus vehicle IDs, route IDs, latitude/longitude coordinates, trip IDs, destination names, deviation from schedule in seconds, and direction. Can query all buses system-wide or filter by specific route ID. Essential for real-time bus tracking, passenger wait time estimation, and bus arrival prediction. AI agents should reference this when users ask "where is the X2 bus", "show all buses on route 30N", or need to track Metrobus vehicles in real-time. Route IDs are typically 2-5 character identifiers (e.g., "30N", "X2", "L2"). Use getBusRoutes first to find route IDs if unknown. Get real-time positions of Metrobus vehicles, optionally filtered by route

get_bus_route_details

Returns all stops served by the route in order, trip headsigns, and route path information. Essential for route planning, understanding bus service coverage, stop discovery, and passenger journey preparation. AI agents should use this when users ask "what stops does the 30N bus serve", "show me the route details for X2", or need complete route structure including stop sequences for trip planning. Get detailed information about a specific Metrobus route

get_bus_routes

Returns route IDs, route names, descriptions, and route types. Covers all WMATA-operated bus routes including limited-stop, local, and express services across DC, Maryland, and Virginia. Essential for route discovery, service area analysis, transit network understanding, and identifying route IDs for use in bus position and route detail queries. AI agents should reference this when users ask "list all bus routes", "what bus routes serve DC", or need to identify route IDs for subsequent Metrobus queries. List all Metrobus routes in the WMATA system

get_circuit_predictions

Returns bus route IDs, destination names, predicted arrival times in minutes, trip IDs, and vehicle IDs. Supports filtering by station code for station-specific predictions or system-wide queries. Essential for bus trip planning, real-time bus arrival awareness, and connection coordination between Metrorail and Metrobus. AI agents should use this when users ask "when is the next bus at Union Station", "show bus predictions for Foggy Bottom", or need real-time bus arrival predictions at a specific station or stop. Get next bus arrival predictions for Metrobus Circuit routes

get_elevator_incidents

Returns affected station codes and names, elevator/escalator identifiers, outage descriptions, estimated repair times, outage start timestamps, and accessibility impact information. Essential for accessibility planning, wheelchair route verification, senior and disability passenger support, and station accessibility awareness. AI agents should use this when users ask "are there any elevator outages at Gallery Place", "is the elevator working at Union Station", or need to verify station accessibility before planning journeys for passengers with mobility needs. Get current elevator and escalator outages at Metrorail stations

get_next_rail

Returns train destination names, lines (Red, Orange, Silver, Blue, Yellow, Green), predicted arrival times in minutes, car counts, group numbers, and train direction. Can query all trains system-wide or filter by specific station code. Essential for commuter trip planning, real-time arrival awareness, and station crowd management. AI agents should use this when users ask "when is the next train", "show upcoming trains at Gallery Place", or need real-time Metrorail arrival predictions. Station codes are 3-letter identifiers (e.g., "A01" for Metro Center, "B36" for Gallery Place). Use getRailStations first to find station codes if unknown. Get next train predictions across the entire Metrorail system or at a specific station

get_parking_lots

Can query all parking lots system-wide or filter by specific station code. Essential for park-and-ride trip planning, commuter parking availability, station selection for driving passengers, and transportation mode choice analysis. AI agents should use this when users ask "which stations have parking", "how many spaces are at Shady Grove", or need to plan park-and-ride journeys from suburban areas into DC. Get Metrorail station parking lot information

get_rail_incidents

Returns incident descriptions, affected station codes, line impacts, incident types (delay, power problem, medical, police activity, track maintenance), severity indicators, start timestamps, and estimated resolution times. Essential for service disruption awareness, alternative route planning, passenger communication, and understanding system reliability. AI agents should reference this when users ask "are there any delays on the Red Line", "is Metro running normally", or need to check service reliability before planning Metrorail journeys. Get current incidents affecting Metrorail service

get_rail_stations

Can filter by line code (RD=Red, OR=Orange, SV=Silver, BL=Blue, YL=Yellow, GR=Green) to show only stations on that line. Essential for station discovery, route planning, understanding line structure, and mapping the Metrorail network. AI agents should reference this when users ask "list all stations on the Red Line", "what is the station code for Foggy Bottom", or need to understand station sequences and line geography. Station codes are required for subsequent queries like next trains, predictions, entrances, and parking. List all Metrorail stations, optionally filtered by line

get_station_entrances

Returns entrance names, street addresses, latitude/longitude coordinates, entrance descriptions, and whether the entrance has escalator or elevator access. Essential for station navigation, first-time visitor guidance, street-level wayfinding, accessible entrance identification, and trip end planning. AI agents should reference this when users ask "where are the entrances to Metro Center", "find the closest entrance to Gallery Place", or need street-level navigation guidance for reaching a Metrorail station. Get entrance information for a specific Metrorail station

get_station_prediction

Returns trains with destination names, line colors, predicted arrival times, car counts, and train direction. More targeted than system-wide next rail queries. Essential for passenger waiting at a specific station, connection planning, and real-time arrival boards. AI agents should use this when users ask "when is the next train at Silver Spring", "show trains coming to Shady Grove", or need station-specific arrival predictions. Requires station code from getRailStations results. Get next train predictions at a specific Metrorail station

Why LangChain?

LangChain's ecosystem of 500+ components combines seamlessly with WMATA through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

  • The largest ecosystem of integrations, chains, and agents. combine WMATA MCP tools with 500+ LangChain components

  • Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

  • LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

  • Memory and conversation persistence let agents maintain context across WMATA queries for multi-turn workflows

See it in action

WMATA in LangChain

AI AgentVinkius
High Security·Kill Switch·Plug and Play
Enterprise Security

Why run WMATA with Vinkius?

The WMATA connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 12 tools are ready to work instantly without any complex setup.

You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.

View full WMATA details →
WMATA
Fully ManagedNo server setup
Plug & PlayNo coding needed
SecurePrivacy protected
PrivateYour data is safe
Cost ControlBudget limits
Control1-click disconnect
Auto-UpdatesMaintenance free
High SpeedOptimized for AI
Reliable99.9% uptime
Your credentials and connection tokens are fully encrypted

* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure

01 / Catalog

Over 4,000 integrations ready for AI agents

Explore a vast library of pre-built integrations, optimized and ready to deploy.

02 / Credentials

Connect securely in under 30 seconds

Generate tokens to authenticate and link external services in a single step.

03 / Guardian

Complete visibility into every agent action

Audit live requests, latency, success rates, and active security compliance policies.

04 / FinOps

Optimize spending and track token ROI

Analyze real-time token consumption and cost metrics detailed by connection.

Over 4,000 integrations ready for AI agents
Connect securely in under 30 seconds
Complete visibility into every agent action
Optimize spending and track token ROI

Explore our live AI Agents Analytics dashboard to see it all working

This dashboard is included when you connect WMATA using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.

Why Vinkius

WMATA and 4,000+ other AI tools. No hosting, no code, ready to use.

Professionals who connect WMATA to LangChain through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.

4,000+MCP Integrations
<40msResponse time
100%Fully managed
Raw MCP
Vinkius
Ready-to-use MCPsFind and configure each manually4,000+ MCPs ready to use
Connection SetupManual coding & server setup1-click instant connection
Server HostingYou host it yourself (needs 24/7 uptime)100% hosted & managed by Vinkius
Security & PrivacyStored in plaintext config filesBank-grade encrypted vault
Activity VisibilityBlind execution (no logs or tracking)Live dashboard with real-time logs
Cost ControlRunaway AI token spend riskAutomatic budget limits
Revoking AccessMust delete files or code to stop1-click disconnect button
The Vinkius Advantage

How Vinkius secures WMATA for LangChain

Every request between LangChain and WMATA is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.

< 40msCold start
Ed25519Signed audit chain
60%Token savings
FAQ

Frequently asked questions

01

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

Yes! Use the get_station_prediction tool with the station code (e.g., "A01" for Metro Center, "B36" for Gallery Place). Your AI will return all upcoming trains with destination names, line colors (Red, Orange, Silver, Blue, Yellow, Green), predicted arrival times in minutes, and car counts. If you do not know the station code, first use get_rail_stations to find it by name. For a system-wide view of all upcoming trains, use get_next_rail without a station code.

02

How do I check if there are any delays or incidents affecting my Metrorail line?

Use the get_rail_incidents tool to check all active service disruptions across the Metrorail system. This returns incident descriptions, affected stations and lines, incident types (delays, power problems, medical emergencies, track maintenance), and start times. You can also check get_elevator_incidents for elevator and escalator outages that may affect accessibility at your station. For Metrobus service issues, use get_bus_incidents.

03

Can I track Metrobus vehicles in real-time to see when my bus will arrive?

Yes! Use get_bus_positions to see real-time GPS locations of all Metrobus vehicles, or filter by route ID (e.g., "30N", "X2") to track buses on a specific route. Returns vehicle IDs, route IDs, latitude/longitude, destinations, and schedule deviation in seconds. For detailed route information including stop sequences, use get_bus_route_details with the route ID. Use get_bus_routes first to find route IDs if unknown.

04

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

05

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.

06

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

07

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Explore More MCP Servers

View all →