How to Use the Arcadia Utility Cloud MCP in Pydantic AI

Q: How do I initialize Arcadia Utility Cloud in Pydantic AI?

Install pydantic-ai-slimmcp via pip. Instantiate MCPToolset with your Vinkius HTTP URL, then include it in the toolsets array when defining your Agent.

Type-safe utility tracking for Pydantic AI. Validate Arcadia Utility Cloud meter readings and statements at runtime.

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Connect Arcadia Utility Cloud MCP to Pydantic AI

Create your Vinkius account to connect Arcadia Utility Cloud to Pydantic AI 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 Arcadia Utility Cloud with Pydantic AI

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Claude

Perplexity

Validate Meter Metrics

The `get_meter_data` tool retrieves raw consumption numbers that Pydantic AI validates against your predefined schemas. If the API returns a string where a float was expected for kilowatt-hours, your agent doesn't try to guess. It fails loudly with a validation error, catching type mismatches before they corrupt your energy dashboards. Building reliable pipelines means never trusting external API structures blindly. You define the exact shape of a valid meter reading in your Python code. The MCP server fetches the data, and the framework ensures every field matches perfectly before your agent processes the numbers.

Audit Accounts via MCP Server

Calling `get_account_check` and `list_accounts` maps out your active utility connections. Your code requests the current state of the Arcadia integration, receiving an array of account dictionaries. Pydantic AI parses these responses, ensuring every account ID and status flag conforms to your strict models. Setting up this connection requires the unified `MCPToolset` approach. Passing your Vinkius HTTP URL to the toolset and adding it to your Agent's toolsets array handles the plumbing. You get model-agnostic utility tracking that works equally well whether you run Claude, GPT-4, or a local Llama instance.

Parse Billing Statements

Executing `list_statements` followed by `get_statement` pulls the metadata for your utility bills. Financial applications demand exact precision, so your agent extracts the invoice amounts and dates through the type-safe framework. Hallucinated fields or missing currency markers trigger immediate runtime exceptions. Relying on the Streamable HTTP transport keeps the MCP connection stable during large data pulls. The Vinkius platform hosts the server externally, managing the complex authentication requirements. Your Pydantic AI application just makes the tool call, receives the validated JSON, and updates your accounting ledger.

Setup guide

Set up Arcadia Utility Cloud 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": {
        "arcadia-utility-cloud-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Arcadia Utility Cloud transactions")
print(result.output)

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Common questions about Arcadia Utility Cloud MCP in Pydantic AI

Install pydantic-ai-slim[mcp] via pip. Instantiate MCPToolset with your Vinkius HTTP URL, then include it in the toolsets array when defining your Agent.

The framework throws a strict validation error at runtime. Your agent will not proceed with corrupted or missing fields, preventing downstream failures in your application.

No, that class is deprecated. You'll want to use the unified MCPToolset approach, which supports both Streamable HTTP and SSE transports natively.

The framework is completely model-agnostic. You can use any supported LLM to call list_statements and process the billing data, provided the model handles function calling well.

The system extracts sensitive invoice totals, billing periods, and account identifiers. Vinkius executes these operations inside an isolated V8 sandbox that requires a single endpoint token, ensuring your financial records are processed ephemerally without persistent storage.

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