How to Use the DNV Renewables MCP in OpenAI Agents SDK

Q: How does the OpenAI Agents SDK handle the 12-hour file deletion limit for DNV Renewables datasets?

Your agent should run downloadorderdata immediately after getorderstatus returns a success state. If you wait longer than 12 hours, the server deletes the file, and your agent will have to place a new order.

Get raw wind and solar resource data directly into your OpenAI Agents SDK pipelines to build safe, production-grade energy models.

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Connect DNV Renewables MCP to OpenAI Agents SDK

Create your Vinkius account to connect DNV Renewables to OpenAI Agents SDK 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 DNV Renewables with OpenAI Agents SDK

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Run site assessments using the DNV Renewables MCP Server

`get_wind_resource_data` pulls raw wind speed and direction metrics for your exact coordinates directly into your agent's context. Your OpenAI Agents SDK setup auto-discovers this tool, meaning your agent instantly knows how to fetch wind data without you writing custom API wrappers. The agent uses `check_data_availability` first to verify if the requested historical window exists in the DNV database. By caching the tools list, the agent skips discovery latency on subsequent calls, making your resource lookups fast.

Calculate energy yields with built-in agent guardrails

`get_energy_yield_estimate` calculates the annual energy production by combining site-specific wind metrics with your target turbine parameters. The OpenAI Agents SDK executes this calculation under strict runtime guardrails, verifying the turbine model inputs before hitting the DNV servers. If the agent tries to pass invalid hub heights or rotor diameters, the SDK blocks the call before it wastes your API credits. You get clean, verified yield estimates returned straight to your downstream financial modeling agents.

Automate climate dataset orders with verified tracing

`place_data_order` initiates an extraction request for heavy mesoscale datasets when your agent needs deep historical climate arrays. Because the OpenAI Agents SDK tracks every tool call on its dashboard, you can monitor the exact latency and payload size of these large data orders. Your agent uses `get_order_status` to poll the processing state and then runs `download_order_data` as soon as the files are ready. This end-to-end extraction pipeline runs completely in the background while keeping an audit trail of every gigabyte requested.

Setup guide

Set up DNV Renewables MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all DNV Renewables tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives DNV Renewables tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate DNV Renewables tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="DNV Renewables Agent",
            instructions="You have access to DNV Renewables tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 OpenAI Agents SDK

Install the package via pip, initialize the server stream using `MCPServerStreamableHttp`, and pass it to your Agent constructor. The SDK automatically registers all eleven wind, solar, and climate tools.

Yes, you can filter the tool list during initialization or use the SDK's built-in guardrails to block specific endpoints like `place_data_order` if you want to prevent unauthorized data purchases.

Your agent should run `download_order_data` immediately after `get_order_status` returns a success state. If you wait longer than 12 hours, the server deletes the file, and your agent will have to place a new order.

Caching the tool list prevents the SDK from querying the MCP Server for tool definitions on every single agent run, which shaves hundreds of milliseconds off your wind and solar data queries.

The server runs in an isolated V8 sandbox that processes your coordinates and order parameters in memory without persistent storage. All data payloads sent to the DNV API are encrypted in transit, and your API tokens are never exposed to the LLM.

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