How to Use the EMT Madrid (Open Data) MCP in LangChain

Q: How do I handle authentication for the EMT Madrid (Open Data) MCP Server in LangChain?

You need to run the login tool first to retrieve an active session token. Pass this token as a header or parameter in subsequent LangChain tool calls like getbusarrivals to keep the connection open.

Q: What is the best way to initialize this MCP Server in LangChain?

Install the langchain-mcp-adapters package and use the multi-server client pointing to the Vinkius endpoint. This registers tools like planbusroute directly into your agent's available toolset.

Feed real-time Madrid transit data directly into your LangChain reasoning loops and multi-step agent chains.

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Connect EMT Madrid (Open Data) MCP to LangChain

Create your Vinkius account to connect EMT Madrid (Open Data) 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 EMT Madrid (Open Data) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build multi-step transit reasoning chains

LangChain agents can coordinate complex travel plans by linking different tools together. Your agent can run `login` to fetch a valid session token, grab live bus schedules with `get_bus_arrivals`, and map out the entire trip using `plan_bus_route` in a single execution loop. This setup lets you build workflows where the output of one step feeds directly into the next. If a bus is delayed, the agent automatically switches to checking bike availability without needing manual intervention.

Track tool execution with LangSmith tracing

Integrating this MCP Server into your LangChain setup gives you complete visibility over every API call. You can monitor latency, token consumption, and the exact payloads returned by `list_bicimad_stations` directly inside your LangSmith dashboard. Debugging failed transit queries becomes simple. You see exactly when a session token expires and how your agent reacts to empty bike stations or rate limits in real time.

Connect transit data with external databases

LangChain lets you pair these Madrid transit tools with over 500 existing integrations. You can write chains that compare live data from `get_bus_arrivals` against historical passenger logs stored in your local SQL database. This combination allows your agent to make smarter predictions. It can recommend alternative routes during peak hours based on past delays and current station status.

Setup guide

Set up EMT Madrid (Open Data) 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 EMT Madrid (Open Data) 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({
    "emt-madrid-open-data-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 EMT Madrid (Open Data) 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 EMT Madrid. 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 EMT Madrid (Open Data) MCP in LangChain

You need to run the `login` tool first to retrieve an active session token. Pass this token as a header or parameter in subsequent LangChain tool calls like `get_bus_arrivals` to keep the connection open.

Yes. You can register the server tools with a LangGraph state agent to build complex, cyclical transit routing workflows. The agent can query `list_bicimad_stations` and dynamically decide whether to plan a bus route based on bike availability.

Install the `langchain-mcp-adapters` package and use the multi-server client pointing to the Vinkius endpoint. This registers tools like `plan_bus_route` directly into your agent's available toolset.

LangChain agents can be configured with custom retry logic and backoff handlers. If `get_bus_arrivals` hits a rate limit, the chain pauses and retries once the window resets.

Vinkius runs the server in a secure, isolated sandbox. Your login credentials and token payloads never touch the LLM provider directly, keeping your transit queries private.

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