What types of wind and solar data are available?

DNV Renewables provides over 40 climate datasets including: Mesoscale wind data (global and regional), Solar irradiance (GHI, DNI, DHI), Reanalysis datasets (ERA5, MERRA-2), Global wind energy atlas, Temperature, pressure, humidity, and more. Data covers onshore and offshore locations worldwide with time series from 1980s to present.

How do I get an API token for DNV Renewables?

Visit the DNV Renewables platform and contact your DNV account manager or visit the EMD/DNV Renewables website to request API access. Once your account is provisioned, you'll receive an API access token from your account dashboard. This token authenticates your requests and is linked to your subscription plan.

How does the data ordering process work?

The data ordering process is: 1) Check availability for your location, 2) Place an order with dataset, coordinates, and time period, 3) Wait ~30 seconds for processing, 4) Check order status until 'success', 5) Download the generated time series file. Note: Download links expire after 12 hours, so download promptly. Rate limit is 10 orders per 10 minutes.

What is mesoscale climate data and why is it useful?

Mesoscale climate data comes from numerical weather prediction models that simulate atmospheric conditions at regional scales (typically 1-50 km resolution). Unlike single-point measurements, mesoscale data provides spatially consistent, long-term time series essential for renewable energy resource assessment. It's used for wind farm siting, solar project planning, and long-term energy yield analysis because it captures multi-decadal climate patterns that short-term measurements miss.

DNV Renewables MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: DNV Renewables
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect DNV Renewables through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to DNV Renewables "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in DNV Renewables?"
    )
    print(result.data)

asyncio.run(main())

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About DNV Renewables MCP Server

Connect to DNV Renewables API (formerly EMD - Energy and Market Data) and bring world-class wind and solar resource assessment intelligence to any AI agent. Access over 40 climate datasets with mesoscale data, energy yield estimates, and time series extraction for renewable energy projects.

Pydantic AI validates every DNV Renewables tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Wind Resource Assessment — Get wind speed, direction, and temperature data for any global location
Solar Resource Assessment — Access GHI, DNI, DHI, temperature, and wind speed for PV project planning
Energy Yield Estimates — Calculate estimated annual energy production (AEP) for wind turbine configurations
Mesoscale Climate Data — Retrieve long-term climate model data for resource assessment
Dataset Catalog — Browse 40+ available climate datasets including mesoscale, reanalysis, and atlas data
Data Availability — Check what data exists for any geographic coordinates before ordering
Data Node Location — Find geographic coverage areas and data nodes for specific datasets
Order Management — Place data orders, track status, and download completed time series files
Global Coverage — Access wind and solar data for onshore and offshore locations worldwide
Custom Time Periods — Request data for specific date ranges from historical archives

The DNV Renewables MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect DNV Renewables to Pydantic AI via MCP

Follow these steps to integrate the DNV Renewables MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from DNV Renewables with type-safe schemas

Why Use Pydantic AI with the DNV Renewables MCP Server

Pydantic AI provides unique advantages when paired with DNV Renewables through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your DNV Renewables integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your DNV Renewables connection logic from agent behavior for testable, maintainable code

DNV Renewables + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the DNV Renewables MCP Server delivers measurable value.

Type-safe data pipelines: query DNV Renewables with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple DNV Renewables tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query DNV Renewables and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock DNV Renewables responses and write comprehensive agent tests

DNV Renewables MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect DNV Renewables to Pydantic AI via MCP:

check_data_availability

Returns available datasets, time periods, and variables. Essential first step before ordering data. Check data availability for wind and solar at a specific location

download_order_data

Order must have status success. Files auto-deleted after 12 hours. Download completed order data file

get_energy_yield_estimate

Uses site-specific wind data and turbine parameters to estimate annual energy production. Get energy yield estimate for a wind turbine at a specific location

get_mesoscale_climate_data

Useful for long-term climate analysis. Get mesoscale climate data for a location

get_order_status

Orders go from pending to processing to success. Once success, a download URL is provided. Files auto-delete after 12 hours. Check status of a previously placed data order

get_solar_resource_data

Essential for PV system design and energy yield analysis. Use when user needs solar irradiance data, is assessing solar resource potential, or wants solar data for PV modeling. Get solar resource data for a specific location

get_wind_resource_data

Essential for wind farm site assessment and energy yield analysis. Use when user needs wind data for a site, is assessing wind resource potential, or wants wind data for energy modeling. Get wind resource data for a specific location

list_all_orders

List all data orders in your account

list_available_datasets

Over 40 datasets available. List all available climate and renewable energy datasets

locate_data_nodes

Useful for understanding spatial resolution and coverage. Locate data nodes for a specific dataset

place_data_order

The API processes the request and generates a downloadable file. Use getOrderStatus to check when complete. Place an order for climate data extraction

Example Prompts for DNV Renewables in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with DNV Renewables immediately.

"Check what wind data is available for a site at 55.5, 12.0."

"Estimate energy yield for a 5MW wind turbine at 55.5, 12.0 with 120m hub height."

"Get solar resource data for a PV site at 35.0, -106.0 (New Mexico)."

Troubleshooting DNV Renewables MCP Server with Pydantic AI

Common issues when connecting DNV Renewables to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

DNV Renewables + Pydantic AI FAQ

Common questions about integrating DNV Renewables MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your DNV Renewables MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.