How to Use the Appliance Energy Calculator MCP in Pydantic AI

Type-safe appliance utility math for Pydantic AI agents.

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Connect Appliance Energy Calculator MCP to Pydantic AI

Create your Vinkius account to connect Appliance Energy Calculator to Pydantic AI — 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 Pydantic AI

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Appliance Energy Calculator MCP

Utility calculations demand exact precision. When your AI client triggers `get_appliance_impact_summary`, it receives a strictly typed breakdown of power usage and financial cost. If the API returns a string instead of a float for the dollar amount, your framework fails loudly. You get a validation error immediately. No silent corruption or hallucinated billing data sneaks into your application.

Validate Daily Usage Math

Stop trusting LLMs to do arithmetic. Pass the wattage and run time into `calculate_daily_consumption`. The endpoint executes the actual math and returns the precise kilowatt-hours used in a day. Your agent handles the routing while the external logic handles the numbers. This separation of concerns ensures your daily load estimates are always mathematically sound.

Project Monthly Cash Costs

Turn those raw energy figures into actual budget numbers. Send the daily usage and local tariff rate to `calculate_monthly_expenses`. It spits out the monthly dollar cost. You define the expected Pydantic schema for the output. The MCP guarantees the final financial projection matches your exact structural requirements before showing it to the user.

Setup guide

Set up Appliance Energy Calculator MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "appliance-energy-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Appliance Energy Calculator tools.",
)

result = await agent.run("List recent Appliance Energy Calculator transactions")
print(result.output)

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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 Pydantic AI

Install the slim package with MCP support. Use the unified MCPToolset class with your HTTP endpoint. Pass the configured toolset into your Agent initialization.

Yes. Every response runs through your defined Pydantic models. If the utility math returns an unexpected format, the framework throws a validation error instantly.

It sure does. The framework is completely model-agnostic. You can connect it to local models or any major provider. The MCP integration works exactly the same way.

You might be using the deprecated MCPServerHTTP class. Switch to the unified MCPToolset approach for reliable Streamable HTTP and SSE transport.

Security is built into the design. The endpoint only receives isolated variables like kilowatt-hours and local tariffs. It never sees user identities or location data. Your inputs are ephemeral.

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