How to Use the WMATA MCP in LlamaIndex

Q: How do I find current WMATA delays for my users with LlamaIndex?

Call getbusincidents first. The tool returns incident descriptions, affected route IDs, and alternative service recommendations. This data populates your index immediately.

Q: Can I track a bus's position in real-time for WMATA using LlamaIndex?

You use getbuspositions to get the coordinates, route IDs, and schedule deviation. This stream of data is indexed, allowing you to query historical or current tracking information.

Q: What station accessibility info can I retrieve for WMATA using LlamaIndex?

Use getelevatorincidents. The tool returns affected station codes and names along with outage descriptions and estimated repair times, making it easy to ground accessibility checks.

Q: Does WMATA support finding all routes for my application using LlamaIndex?

Yes. Running getbusroutes provides a list of route IDs, names, and descriptions covering the entire DC/MD/VA area. This foundational data is ideal for indexing.

Q: What data type does this WMATA server touch regarding incidents?

The getrailincidents tool touches service disruption information, including incident descriptions, affected station codes, and estimated resolution times. This structured data is highly valuable for RAG applications.

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Connect WMATA MCP to LlamaIndex

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Setup WMATA with LlamaIndex

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Indexing Metrobus Service Status

When you index bus disruptions, use `get_bus_incidents`. This tool gives your knowledge base incident descriptions, affected route IDs, and detour information. Your agent can then answer complex questions like 'is route 30N running normally?' based on indexed data. This indexing capability means that even past WMATA service reports are searchable. The output provides specific details about the nature of the disruption—whether it's construction or a mechanical failure.

Creating Searchable Station Details

Need to know what stops a route serves? Index the results from `get_bus_route_details`. This tool returns all stops in order, along with trip headsigns and route path info. Your RAG application can then answer 'what stops does X2 serve?' accurately. For rail service, you use `get_rail_stations` to populate your index with line codes (Red, Blue, etc.) and station names, allowing users to query the entire WMATA network structure.

Predicting Real-Time Transit Arrivals

To predict arrivals, use `get_circuit_predictions` or `get_station_prediction`. These tools give predicted arrival times in minutes for Metrobus and Metrorail trains, respectively. Your indexed data combines this with station codes. This means if a user asks 'when is the next bus at Union Station,' your LlamaIndex client can ground that answer using recent API call results.

Setup guide

Set up WMATA MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all WMATA MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to WMATA tools.",
)
response = await agent.run("List recent WMATA data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WMATA. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about WMATA MCP in LlamaIndex

Call `get_bus_incidents` first. The tool returns incident descriptions, affected route IDs, and alternative service recommendations. This data populates your index immediately.

You use `get_bus_positions` to get the coordinates, route IDs, and schedule deviation. This stream of data is indexed, allowing you to query historical or current tracking information.

Use `get_elevator_incidents`. The tool returns affected station codes and names along with outage descriptions and estimated repair times, making it easy to ground accessibility checks.

Yes. Running `get_bus_routes` provides a list of route IDs, names, and descriptions covering the entire DC/MD/VA area. This foundational data is ideal for indexing.

The `get_rail_incidents` tool touches service disruption information, including incident descriptions, affected station codes, and estimated resolution times. This structured data is highly valuable for RAG applications.

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