How to Use the Corrently Energy MCP in Pydantic AI

Type-safe energy automation with Pydantic AI. Use Corrently Energy to get verified grid data directly into your Python models.

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Connect Corrently Energy MCP to Pydantic AI

Create your Vinkius account to connect Corrently Energy 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 Corrently Energy with Pydantic AI

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Claude

Perplexity

Validated energy account balance

Use `get_stromkonto_balance` to verify account credits and green energy certificates. Pydantic AI validates every response, ensuring your agent never processes malformed data. This is for when you need to be sure about the account state. If the API returns garbage, the agent stops immediately, preventing any calculation errors.

Accurate CO2 grid forecasting

Query `get_co2_prediction` to get future carbon intensity for any German location. The tool returns structured data that maps perfectly to your Pydantic models. Your agent plans ahead based on these forecasts. It knows exactly when the grid will be clean, allowing it to schedule tasks for the lowest carbon footprint.

Find the best consumption hours

Call `get_best_hour` to get the perfect window for energy-intensive work. It returns the optimal time slots based on current renewable energy availability. You avoid the peak times when energy is dirty or expensive. The agent receives a clear, validated response, making the decision process simple and reliable.

Setup guide

Set up Corrently Energy 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": {
        "corrently-energy-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Corrently Energy 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 Corrently Energy. 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 Corrently Energy MCP in Pydantic AI

Every MCP response is checked against your Pydantic models at runtime. If the energy data doesn't match the expected format, the agent throws a validation error.

Yes. You can use SSE or Streamable HTTP transports. The server is designed to work with the unified toolset approach in your Python code.

Absolutely. Pydantic AI is model-agnostic. You can pair this MCP server with local LLMs or cloud models without changing your tool integration.

It gives your agent direct access to real-time German energy market data. You stop writing custom scrapers and use a standardized, managed interface instead.

The server only processes the 5-digit zip code needed for the energy query. No personal information or usage history is stored, maintaining strict data privacy.

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