How to Use the Corrently Energy MCP in LangChain

Q: Can I trace Corrently Energy tool calls using LangChain and LangSmith?

Yes, every execution of tools like getmarketdata or getbesthour shows up in your LangSmith dashboard with full inputs and outputs. You can inspect the exact latency and token cost of your German energy queries in real time.

Q: How does the Corrently Energy MCP Server handle rate limits during complex LangChain runs?

The server accepts an optional API token in the getgsiprediction tool to prevent rate limiting during heavy multi-step chain executions. You pass this token in your client configuration when initializing the server connection.

Q: What is the best way to schedule appliance runs using this server?

You use the getbesthour tool to find the cleanest or cheapest window, then pass that output to createenergyschedule inside your agent's execution loop. This lets the agent programmatically schedule German hardware based on live market conditions.

Run multi-step energy automation chains in LangChain using this MCP Server for German grid data and CO2 forecasts.

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Connect Corrently Energy MCP to LangChain

Create your Vinkius account to connect Corrently Energy 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 Corrently Energy with LangChain

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Build multi-step energy chains in LangChain

This MCP Server exposes tools like `create_energy_schedule` to let your LangChain agents build dynamic energy automation pipelines. The agent reads the current grid state, calculates optimal runtimes, and writes a device schedule in a single, observable execution path. By feeding the output of `get_market_data` directly into your decision logic, your LangChain chain handles fluctuating German power prices without hardcoded rules. You see every step of the calculation inside LangSmith, tracking exactly how the agent decided to schedule the load.

Smart PHEV charging logic based on real-time grid data

The `get_phev_charge_or_fuel` tool gives your LangChain agent the exact data needed to decide whether a hybrid vehicle should charge from the grid or burn fuel. Your agent runs this calculation by checking the regional grid status before making the final call. You combine this with `get_co2_meter` in a LangGraph state machine to route charging decisions dynamically based on real-time carbon intensity. The framework passes these values between nodes, letting you build custom green-charging workflows that react to live German zip code data.

Predict solar output and calculate offsets

The `get_solar_prediction` tool feeds expected photovoltaic generation directly into your LangChain forecasting chains. Your agent takes the solar capacity in kWp and matches it against local weather forecasts to predict actual generation windows. When generation drops, the agent triggers `calculate_co2_offset` to find the exact carbon compensation cost for any remaining emissions. This chain gives your application a verifiable way to balance green energy targets with actual grid realities.

Setup guide

Set up Corrently Energy 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 Corrently Energy 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({
    "corrently-energy-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 Corrently Energy 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 Corrently Energy. 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 Corrently Energy MCP in LangChain

You pass the five-digit zip code as a string argument directly to tools like `get_gsi_prediction` inside your LangChain tool-calling agent. The framework handles the JSON schema validation automatically, ensuring the agent format matches the server's requirements.

Yes, every execution of tools like `get_market_data` or `get_best_hour` shows up in your LangSmith dashboard with full inputs and outputs. You can inspect the exact latency and token cost of your German energy queries in real time.

The server accepts an optional API token in the `get_gsi_prediction` tool to prevent rate limiting during heavy multi-step chain executions. You pass this token in your client configuration when initializing the server connection.

You use the `get_best_hour` tool to find the cleanest or cheapest window, then pass that output to `create_energy_schedule` inside your agent's execution loop. This lets the agent programmatically schedule German hardware based on live market conditions.

The Vinkius sandbox isolates your runtime, meaning your Stromkonto account balance retrieved via `get_stromkonto_balance` and regional zip codes never leak to external third parties. All communication between LangChain and the server happens over a secure, single-endpoint token.

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