How to Use the Awattar MCP in Pydantic AI

Q: How does Pydantic AI handle a failure in the Awattar API?

If getmarketdata returns malformed data or fails to connect, Pydantic AI raises a validation or connection error immediately. This prevents your agent from making automated decisions based on stale or missing pricing information.

Q: Can I use Pydantic AI to write Awattar YAML files safely?

Yes, by calling getcurrentyaml through the toolset, you get structured price statistics. Pydantic AI validates the structure of this output before your agent attempts to write it to your local configuration.

Get type-safe Awattar electricity pricing directly in Pydantic AI to build crash-proof home energy automations.

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Connect Awattar MCP to Pydantic AI

Create your Vinkius account to connect Awattar 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 Awattar with Pydantic AI

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Validate Awattar pricing models at runtime

The `get_market_data` tool fetches raw EPEX Spot pricing arrays, which Pydantic AI immediately validates against strict Python type schemas. If the upstream European grid API returns an unexpected format, your agent fails instantly instead of executing a bad schedule. This type-safe validation prevents your system from misinterpreting a price spike as a discount. You avoid costly automated charging mistakes because the agent refuses to act on unverified or corrupted pricing payloads.

Parse structured YAML stats with Pydantic AI

The `get_current_yaml` tool delivers pre-formatted YAML statistics that represent the current day's price curve. Pydantic AI parses these blocks to ensure the YAML structure matches your local home automation requirements before writing to disk. By verifying the schema at the runtime boundary, you prevent malformed syntax from crashing your Home Assistant supervisor. The agent reads the validated YAML block and safely updates your local configuration entities.

Connect Pydantic AI to this energy MCP Server

You connect this energy MCP Server directly into your Pydantic AI codebase using the unified `MCPToolset` class. The framework automatically discovers the server's tools and maps their inputs and outputs to type-safe Python objects. You run the server externally and connect via streamable HTTP or SSE transports. This separation of concerns keeps your core agent logic lightweight while ensuring reliable access to real-time spot market rates.

Setup guide

Set up Awattar 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": {
        "awattar-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Awattar transactions")
print(result.output)

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Common questions about Awattar MCP in Pydantic AI

If `get_market_data` returns malformed data or fails to connect, Pydantic AI raises a validation or connection error immediately. This prevents your agent from making automated decisions based on stale or missing pricing information.

Yes, by calling `get_current_yaml` through the toolset, you get structured price statistics. Pydantic AI validates the structure of this output before your agent attempts to write it to your local configuration.

The integration supports both Streamable HTTP and SSE transports. You configure the connection by passing the external Vinkius server URL directly to the `MCPToolset` constructor.

Set up a cron job or task runner to trigger your agent after 14:00 CET. The agent calls `get_market_data` to fetch the newly published spot prices and updates your local scheduling database.

The server only queries public EPEX Spot market rates and generates structural YAML templates. No sensitive telemetry from your home, smart plug power logs, or utility account details are ever touched or processed.

Use it with your favorite AI tools

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