How to Use the DNV Renewables MCP in CrewAI

Q: How does this DNV Renewables MCP Server handle bulk climate orders in CrewAI?

Your procurement agent initiates the order using placedataorder. Because these extractions take time, a separate monitoring agent can poll getorderstatus in the background, freeing up other agents to continue analyzing existing datasets.

Q: What happens if an agent requests data for an unsupported region?

The agent should first call checkdataavailability or listavailabledatasets. If the requested coordinates fall outside the supported coverage, the tool returns a clear error, allowing the agent to adjust its search coordinates.

Deploy specialized agent teams to analyze solar and wind potential with CrewAI.

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Connect DNV Renewables MCP to CrewAI

Create your Vinkius account to connect DNV Renewables to CrewAI 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 CrewAI

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Multi-agent wind farm prospecting

CrewAI lets you set up a specialized research team. One agent can use `check_data_availability` to scan coordinates, while a second agent calls `get_wind_resource_data` to analyze the wind speed patterns. This collaborative structure prevents a single agent from getting overwhelmed by raw numbers. The agents share context in memory, ensuring the wind analyst always works with verified coordinates from the researcher.

Automated solar yield planning via MCP Server

Designing solar arrays requires matching local irradiance with system specs. Your solar agent can use `get_solar_resource_data` to fetch historical solar patterns for any location. A second engineering agent in the crew then takes that data and runs `get_energy_yield_estimate` to calculate the expected annual production. This division of labor matches the way human engineering teams operate.

Autonomous climate data procurement

Getting mesoscale climate data usually requires manual intervention. With this MCP Server, your operations crew can call `place_data_order` to request the dataset automatically. A monitoring agent tracks the process by checking `get_order_status` until it succeeds. The agent then calls `download_order_data` to download the completed file, making the entire acquisition loop fully autonomous.

Setup guide

Set up DNV Renewables MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke DNV Renewables tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="DNV Renewables Analyst",
    goal="Access and analyze DNV Renewables data via MCP.",
    backstory="Expert analyst with direct DNV Renewables access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent DNV Renewables transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="DNV Renewables Analyst",
        goal="Access and analyze DNV Renewables data via MCP.",
        backstory="Expert analyst with direct DNV Renewables access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent DNV Renewables transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

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 CrewAI

The easiest way is passing your Vinkius endpoint URL directly into the agent's mcps list during initialization. For more advanced setups, use MCPServerHTTP from crewai.mcp to filter exactly which tools each agent can access.

Yes, CrewAI's shared memory allows agents to pass tool outputs to each other. Once the research agent calls `get_wind_resource_data`, the analytical agent can immediately use those wind metrics without making a second API call.

Your procurement agent initiates the order using `place_data_order`. Because these extractions take time, a separate monitoring agent can poll `get_order_status` in the background, freeing up other agents to continue analyzing existing datasets.

The agent should first call `check_data_availability` or `list_available_datasets`. If the requested coordinates fall outside the supported coverage, the tool returns a clear error, allowing the agent to adjust its search coordinates.

All requests are routed through an ephemeral MCP connection on Vinkius, meaning your turbine specs and solar coordinates are never stored on external servers. Completed climate and wind files are securely downloaded and then automatically purged from the host within 12 hours.

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