How to Use the Appliance Energy Calculator MCP in LlamaIndex

Index real home power data into your LlamaIndex vector store using this Appliance Energy Calculator MCP.

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Connect Appliance Energy Calculator MCP to LlamaIndex

Create your Vinkius account to connect Appliance Energy Calculator to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Appliance Energy Calculator with LlamaIndex

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Index daily energy usage into LlamaIndex

The `calculate_daily_consumption` tool calculates the energy consumption of an appliance in one day to provide a baseline for your knowledge base. Your LlamaIndex pipeline takes this daily kilowatt-hour output and indexes it directly into your local vector store for semantic search. This means your agent can query past consumption logs to find trends without querying the live API every time. You get answers grounded in your actual historical usage data.

Ground financial RAG queries with this MCP Server

The `calculate_monthly_expenses` tool calculates the monthly electricity cost for a given energy usage, giving your RAG pipeline concrete pricing data. This tool lets your agent answer complex questions about utility budgets using real financial projections. By storing these monthly calculations in your index, you prevent your agent from hallucinating energy costs. Your search queries will return actual calculated dollar amounts instead of vague estimates.

Build searchable appliance profiles

The `get_appliance_impact_summary` tool gives you a complete energy and cost summary for an appliance, compiling lifetime operating costs and daily demands. Your agent indexes these summaries to build a searchable catalog of home machinery. Using this MCP to index data means you can ask your agent which appliance costs the most to run. It retrieves the exact summary from your vector store to give you a factual, data-driven answer.

Setup guide

Set up Appliance Energy Calculator MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Appliance Energy Calculator MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Appliance Energy Calculator tools.",
)
response = await agent.run("List recent Appliance Energy Calculator data")

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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 Appliance Energy Calculator. 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 Appliance Energy Calculator MCP in LlamaIndex

Install the required adapter using pip install llama-index-tools-mcp first. Then, initialize the BasicMCPClient and pass the tools to your FunctionAgent.

Yes. You can index the outputs of the calculations directly into a vector store to make them searchable.

Yes. You can call the tools asynchronously using the standard LlamaIndex tool adapters to keep your search indexes updated in real time.

It provides hard math for energy costs. Your agent queries the structured outputs instead of guessing how much a refrigerator costs to run.

Yes. This MCP processes only numeric inputs like wattages and run times, meaning your private home configuration details never leave your local environment.

Use it with your favorite AI tools

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

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