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`.

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.

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

Name: WMATA
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 WMATA 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="wmata_agent",
            tools=tools,
            system_message=(
                "You help users with WMATA. "
                "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 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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use WMATA 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

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

The WMATA 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 WMATA to AutoGen via MCP

Follow these steps to integrate the WMATA 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 WMATA automatically

Why Use AutoGen with the WMATA MCP Server

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

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

Role-based architecture lets you assign WMATA 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 WMATA tool calls

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

WMATA + AutoGen Use Cases

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

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

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

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

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

WMATA MCP Tools for AutoGen (12)

These 12 tools become available when you connect WMATA to AutoGen via MCP:

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

Example Prompts for WMATA in AutoGen

Ready-to-use prompts you can give your AutoGen agent to start working with WMATA immediately.

"Show me the next trains arriving at Gallery Place station."

"Are there any incidents affecting the Red Line right now?"

"Where is the closest entrance to Metro Center station from 12th Street?"

Troubleshooting WMATA MCP Server with AutoGen

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

McpWorkbench not found

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

WMATA + AutoGen FAQ

Common questions about integrating WMATA 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 WMATA 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.