How to Use the Awattar MCP in LangChain

Q: How do I pass Awattar tools to a LangChain agent?

You install the langchain-mcp-adapters package and use the MultiServerMCPClient to connect to the server. From there, call client.gettools() and pass the resulting list directly into your agent initializer.

Q: Can my LangChain agent use Awattar price data to write Home Assistant automations?

Yes, the agent can call getcurrentyaml to fetch properly formatted YAML stats. It then uses that structured data to write or update your local automation files without syntax errors.

Feed real-time EPEX Spot electricity prices directly into your LangChain chains to automate smart home loads when power is cheapest.

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Connect Awattar MCP to LangChain

Create your Vinkius account to connect Awattar 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 Awattar with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build Multi-Step Energy Chains with LangChain

The `get_market_data` tool fetches hourly EPEX Spot prices so your LangChain agents can map out the cheapest windows for heavy appliance runs. You get raw market numbers directly inside your chain, allowing the model to calculate exact cost differences before triggering any physical hardware. This MCP server setup lets you pipe those calculated Awattar price windows directly into downstream LangChain API nodes without manual data translation. You can monitor the entire execution path in LangSmith to see exactly how your agent weighed different hourly rates before making a decision.

Feed Clean YAML Configs to Home Assistant

The `get_current_yaml` tool formats the latest Awattar price metrics into clean YAML blocks that fit perfectly into your Home Assistant automation scripts. Your LangChain agent reads this structured text and immediately drafts configuration updates based on current grid rates. Using this dedicated MCP tool ensures your LLM chains don't waste tokens parsing messy HTML or trying to guess the correct YAML indentation. The model gets a clean, structured payload that it can write directly to your config files.

Trace Energy Decisions via LangSmith

Calling the `get_market_data` tool within a LangChain ReAct loop gives you full visibility into how your agent analyzes changing EPEX Spot rates. Every LangChain decision to delay an EV charger based on Awattar pricing gets logged with precise token costs and latency metrics. This level of observability ensures you can debug why an agent chose a specific hour for energy consumption. You get a transparent audit trail of how the MCP integration interacted with your home automation system during price spikes.

Setup guide

Set up Awattar 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 Awattar 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({
    "awattar-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 Awattar transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Awattar MCP in LangChain

You install the `langchain-mcp-adapters` package and use the `MultiServerMCPClient` to connect to the server. From there, call `client.get_tools()` and pass the resulting list directly into your agent initializer.

Yes, the agent can call `get_current_yaml` to fetch properly formatted YAML stats. It then uses that structured data to write or update your local automation files without syntax errors.

Yes, because the server exposes standard tools that map directly to LangChain's tool interface. Every call to `get_market_data` shows up in your LangSmith dashboard with full input and output payloads.

Your agent will receive the last cached price stats or an empty response from the API. You should configure your chain to fall back to standard local rates if the network request fails.

This MCP server only fetches public EPEX Spot electricity prices and formats them into YAML. It never collects or transmits your private home energy consumption metrics, local IP addresses, or smart plug identifiers.

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