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 CrewAI 11 tools — connect in under 2 minutes

Name: DNV Renewables
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to DNV Renewables through Vinkius, pass the Edge URL in the `mcps` parameter and every DNV Renewables tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="DNV Renewables Specialist",
    goal="Help users interact with DNV Renewables effectively",
    backstory=(
        "You are an expert at leveraging DNV Renewables tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in DNV Renewables "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, DNV Renewables becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call DNV Renewables tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 11 tools from DNV Renewables

Why Use CrewAI with the DNV Renewables MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with DNV Renewables through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

DNV Renewables + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries DNV Renewables for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries DNV Renewables, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain DNV Renewables tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries DNV Renewables against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

DNV Renewables MCP Tools for CrewAI (11)

These 11 tools become available when you connect DNV Renewables to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

DNV Renewables + CrewAI FAQ

Common questions about integrating DNV Renewables MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.