How to Use the CTA MCP in LangChain

Q: Can I trace transit queries using LangSmith?

Absolutely. Every call to tools like getroutestatus shows up in your LangSmith dashboard with full inputs, outputs, and latency metrics.

Get real-time Chicago transit data straight into your LangChain runs to build smart, context-aware city guides.

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Connect CTA MCP to LangChain

Create your Vinkius account to connect CTA to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup CTA with LangChain

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Track live CTA train arrivals in your chains

`get_train_arrivals` pulls live arrival times, run numbers, and track status directly from the CTA L system. Your agent uses these raw timestamps to calculate transfer windows and coordinate multi-leg journeys without hardcoding transit schedules. This tool plugs into your existing LangChain agent as a standard run step. It feeds clean JSON into your prompt templates, letting the LLM decide if a passenger can make the connection at Clark/Lake.

Build multi-step transit routing with LangChain

`get_bus_stops` maps out all stops for any active Chicago bus route, complete with geographic coordinates and stop sequences. Combine this with `get_bus_predictions` to build an agent that figures out exactly when the next bus arrives at a specific corner. Since LangChain manages the state across your chains, the output from one tool feeds directly into the next. Your agent handles the logic of finding the stop first, then pulling the real-time prediction in a single execution loop.

Monitor Chicago transit delays inside your MCP Server

`get_service_alerts` checks active disruptions, detours, and maintenance work across the entire CTA network. Your agent evaluates these alerts to redirect users before they walk to a closed train platform. By hooking this MCP Server up to your LangChain setup, you get instant access to live transit status. The agent scans the raw alert text, filters by route, and outputs clear alternative travel plans.

Setup guide

Set up CTA MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes CTA tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cta-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent CTA transactions"
    })
    print(result["messages"][-1].content)

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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 CTA. 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 CTA MCP in LangChain

Install the adapter package via pip and initialize the client with the Vinkius endpoint. Pass the tools directly to your agent initialization block to start querying transit data.

Yes, the server provides separate tools for both transit systems. Your agent calls `get_train_positions` or `get_bus_vehicles` depending on what the user asks for.

Vinkius manages the connection and authentication details on its secure servers. Your LangChain code just makes standard tool calls without worrying about raw API keys or rate-limiting headers.

Absolutely. Every call to tools like `get_route_status` shows up in your LangSmith dashboard with full inputs, outputs, and latency metrics.

Your transit queries, stop IDs, and transit coordinates run through a secure, isolated sandbox. Vinkius does not store your location history or search logs, keeping your routing requests completely private.

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