How to Use the DNV Renewables MCP in Pydantic AI

Q: How do I configure Pydantic AI to use the DNV Renewables MCP Server?

Install the slim MCP package, instantiate MCPToolset with your server's HTTP endpoint, and pass it to your Agent's toolsets list. The framework automatically maps the wind, solar, and climate tools to typed Python functions.

Q: How does Pydantic AI handle the 12-hour file expiration on data downloads?

Your agent should track the timestamp returned by getorderstatus and run downloadorderdata immediately. You can write a Pydantic model to validate that the download occurs well within the 12-hour deletion window enforced by the MCP Server.

Run type-safe renewable energy modeling with Pydantic AI and the DNV Renewables MCP Server.

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Setup DNV Renewables with Pydantic AI

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Get type-safe energy yield estimates

`get_energy_yield_estimate` calculates the annual energy output for a specific wind turbine based on localized wind resources. Pydantic AI validates the returned estimate object against a strict schema at runtime, ensuring your financial models never ingest corrupted or missing float values. If the DNV API returns an unexpected data structure, the framework raises a validation error instantly instead of letting your agent hallucinate a number. This strict validation makes it safe to feed these estimates directly into production underwriting systems.

Verify wind and solar datasets before ordering

`check_data_availability` queries the DNV database to confirm which variables and time ranges are accessible for your coordinates. Your Pydantic AI agent uses this tool to check data coverage, parsing the response into strongly-typed Python models. Once coverage is verified, the agent uses `list_available_datasets` to cross-reference available files against your project requirements. This double-check prevents your agent from placing incorrect orders or querying empty coordinates.

Manage climate data orders with strict runtime checks

`place_data_order` kicks off a climate data extraction request on the DNV servers. Pydantic AI monitors this asynchronous process by feeding the output of `get_order_status` into a typed state machine that tracks the transition from pending to success. When the order status changes to success, the agent triggers `download_order_data` to fetch the file. Because every step is validated against Pydantic schemas, you can trust that the downloaded file paths and metadata are perfectly formatted.

Setup guide

Set up DNV Renewables 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": {
        "dnv-renewables-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent DNV Renewables 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 DNV Renewables. 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 DNV Renewables MCP in Pydantic AI

Install the slim MCP package, instantiate `MCPToolset` with your server's HTTP endpoint, and pass it to your Agent's `toolsets` list. The framework automatically maps the wind, solar, and climate tools to typed Python functions.

The framework will immediately raise a validation error at runtime, preventing the agent from passing bad or incomplete wind speed arrays to your downstream calculations.

Yes, the framework is model-agnostic, meaning you can run your wind and solar prospecting agents using local models or commercial APIs while maintaining strict type safety on all tool outputs.

Your agent should track the timestamp returned by `get_order_status` and run `download_order_data` immediately. You can write a Pydantic model to validate that the download occurs well within the 12-hour deletion window enforced by the MCP Server.

Your coordinates and order parameters are processed within secure, isolated V8 sandboxes that delete all session data after execution. The server never logs your coordinates or API keys, ensuring complete data privacy for your prospecting sites.

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