How to Use the ENTSO-E MCP in LangChain

Q: How does LangChain parse the XML responses from ENTSO-E?

The framework handles the raw XML output from tools like getgenerationoutages by passing the text directly to the LLM or a custom parser in your chain. You can write a simple output parser to convert the XML structure into clean JSON before feeding it to subsequent steps.

Feed live European grid metrics straight into your LangChain ReAct agents to automate power market analysis.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect ENTSO-E MCP to LangChain

Create your Vinkius account to connect ENTSO-E 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.

GDPR Free for Subscribers

Setup ENTSO-E with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain live grid metrics into multi-step LangChain ReAct loops

The `get_actual_generation` tool lets your LangChain agents pull live power source data directly into their execution loops. By linking this MCP Server with downstream analysis steps, your agent calculates regional imbalances on the fly. If your agent spots a generation deficit, it immediately triggers `get_balancing_prices` to assess market volatility. You can trace every single tool execution, latency metric, and token count inside LangSmith to keep your energy trading workflows fully visible.

Map transmission bottlenecks using LangChain agent chains

The `get_transmission_outages` tool exposes grid congestion and active network maintenance schedules directly to your LangChain agent chains. Your agent can identify physical constraints before they impact active trading strategies. By feeding this physical data into `get_crossborder_flows`, the agent maps scheduled energy transfers against real-world grid limits. This setup turns raw XML outputs into actionable inputs for downstream database steps or notification chains.

Validate day-ahead forecasts against physical limits

The `get_day_ahead_prices` tool works alongside `get_wind_solar_forecast` inside your LangChain pipelines to detect pricing anomalies before markets clear. Your agent compares day-ahead expectations with physical realities to verify market health. Once the agent identifies a discrepancy, it pulls `get_day_ahead_load` to verify if regional demand projections align with historical patterns. This approach ensures your decision-making pipelines rely on verified physical constraints rather than isolated pricing signals.

Setup guide

Set up ENTSO-E 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 ENTSO-E 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({
    "entso-e-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 ENTSO-E transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ENTSO-E. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect ENTSO-E now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about ENTSO-E MCP in LangChain

You can configure custom retry logic inside your LangChain runnable or handle rate limits directly within the MultiServerMCPClient setup. This ensures that heavy calls to `get_actual_load` or `get_balancing_prices` do not crash your active execution chain.

Yes, every tool call from this MCP Server shows up in LangSmith with complete input and output details. You will see the exact bidding zone codes sent to `get_crossborder_flows` and the raw XML payload returned by the API.

The framework handles the raw XML output from tools like `get_generation_outages` by passing the text directly to the LLM or a custom parser in your chain. You can write a simple output parser to convert the XML structure into clean JSON before feeding it to subsequent steps.

Absolutely, you can register this MCP Server alongside a SQL database tool in your LangChain agent. This lets the agent pull historical capacities via `get_installed_generation` and write the values directly to your local database in a single run.

Vinkius manages your credential in an isolated V8 sandbox, meaning your LangChain client only needs a single secure endpoint token to access the grid data. Your raw API key is never exposed to the LLM or stored in the agent's memory space, keeping your XML power market queries safe.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript