How to Use the U.S. EIA Energy Data MCP in LangChain

Build multi-step energy analysis chains with LangChain.

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Connect U.S. EIA Energy Data MCP to LangChain

Create your Vinkius account to connect U.S. EIA Energy 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 U.S. EIA Energy Data with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Sequential Energy Auditing

You can build complex reasoning pipelines where the agent decides which tools to call and in what order. For instance, an agent might first call `list_energy_categories` to scope the data, then use that output to refine a request for specific metrics using `get_energy_series_data`.

Comparing Fuel Sources

The framework lets you chain together disparate data points into one conclusion. You can start by listing U.S. coal production via `list_coal_production`, and immediately follow that up with a call to `list_natural_gas_production` to compare historical trends in the same run.

Pricing Analysis Chains

You can establish multi-step processes for financial data. Run `list_petroleum_prices` first. Then, use that price output as input for a second step—maybe checking regional variations using `list_regional_fuel_prices`—to get an immediate picture of the market.

Setup guide

Set up U.S. EIA Energy 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 U.S. EIA Energy 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({
    "us-eia-energy-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 U.S. EIA Energy 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 U.S. EIA Energy Data. 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 U.S. EIA Energy Data MCP in LangChain

LangChain manages complexity by allowing your agent to decide on a sequence of tool calls. You don't write the whole script; you just define the goal, and the client determines whether it needs `list_electricity_generation` followed by a quick audit using `quick_us_energy_audit`.

Absolutely. You can build chains that move from broad data gathering—like calling `list_renewable_energy_stats`—to specific analysis, such as checking the latest gas supply numbers with `list_natural_gas_production`.

You connect your agent to this MCP Server and then expose the tool definitions. Your AI client handles the connection, letting you use tools like `list_coal_production` or `get_energy_series_data` as nodes in a multi-step workflow.

Yes. Since the client supports session management, you can use `client.session()` to maintain context across multiple calls. This is useful if you're tracking changing fuel prices over several days.

The core data includes time-series information on energy metrics, specific breakdowns of coal and natural gas production, and current retail price indexes for petroleum products.

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