How to Use the DNV Renewables MCP in AutoGen

Q: Can AutoGen agents challenge DNV Renewables data results?

Yes, you can have a reviewer agent analyze the output from getenergyyieldestimate. If the results don't meet your internal thresholds, the agent can demand a re-evaluation or suggest a different site.

Deploy multi-agent teams in AutoGen to debate and verify wind farm viability using DNV Renewables data.

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Connect DNV Renewables MCP to AutoGen

Create your Vinkius account to connect DNV Renewables to AutoGen 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 AutoGen

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Debate site feasibility in AutoGen

Assign one agent to fetch data via `get_wind_resource_data` while another agent critiques the site based on local terrain findings. The agents negotiate until they reach a consensus on project viability. This system forces your agents to look at the data from multiple angles. It catches potential errors in site selection before you commit to a full assessment.

Automate data order workflows for agents

Let a coordinator agent call `place_data_order` when the project team decides a site is promising. The agent handles the status polling using `get_order_status` without human input. If the data isn't ready, the agent waits and reports back to the team. It keeps your project moving without manual monitoring of long-running data requests.

Cross-reference datasets with agent teams

Use `list_available_datasets` to give your AutoGen agents a full view of what's possible. They can decide which climate datasets are necessary to validate your energy yield assumptions. One agent acts as the data specialist, ensuring the team uses the correct datasets for the specific region. They reconcile different data sources to build a better project case.

Setup guide

Set up DNV Renewables MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes DNV Renewables tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="DNV Renewables_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent DNV Renewables data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="DNV Renewables_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent DNV Renewables data")
    print(result.messages[-1].content)

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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 AutoGen

Pass the tool list to your AssistantAgent constructor using the AutoGen MCP extension. Your agents will automatically learn how to invoke these functions during their group discussions.

Yes, you can have a reviewer agent analyze the output from `get_energy_yield_estimate`. If the results don't meet your internal thresholds, the agent can demand a re-evaluation or suggest a different site.

The agents can debug each other's tool calls. If a request fails, the team discusses the error logs and decides whether to retry the operation or try a different coordinate set.

Every tool call is routed through an authenticated Vinkius session. Your AutoGen agents only access the datasets you permit, maintaining a strict boundary around your confidential project information.

Agents share the climate and yield values retrieved from the server. This data is handled in memory during the conversation, ensuring that no raw site information is leaked outside your agentic environment.

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