How to Use the DNV Renewables MCP in Google ADK

Q: How do I initialize the DNV Renewables MCP Server inside Google ADK?

Install the ADK, set up your server parameters using StreamableHttpServerParameters with your host URL, and pass the resulting toolset to your LlmAgent. Your Gemini models can immediately call the wind and solar tools.

Q: Can my Google ADK agent join DNV Renewables wind data with BigQuery tables?

Yes, your agent can call getwindresourcedata to fetch localized wind speeds, format the JSON payload, and load it directly into a BigQuery table using native Google Cloud tools.

Q: How does Google ADK handle the asynchronous order process for this MCP Server?

The agent uses a polling loop that checks getorderstatus at set intervals. Because Gemini has a massive context window, the agent can retain the entire order history and coordinate details throughout the polling cycle.

Q: Can I limit the tools exposed to my Google ADK agent?

Yes, you can use the toolnames filter in the McpToolset configuration to expose only prospecting tools like getsolarresourcedata while hiding ordering tools.

Connect DNV Renewables resource data to your Google ADK agents to feed solar and wind metrics directly into BigQuery.

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Connect DNV Renewables MCP to Google ADK

Create your Vinkius account to connect DNV Renewables to Google ADK 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 Google ADK

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Map regional wind resources with Google ADK

`get_wind_resource_data` retrieves localized wind velocity and direction arrays directly into your Gemini long-context window using our managed MCP Server. Google ADK lets your agent ingest millions of tokens of historical wind data, making it easy to analyze decades of climate patterns without hitting context limits. The agent uses `locate_data_nodes` to pinpoint the closest spatial resolution coordinates for your project site. This lets you combine DNV's grid points with your existing GIS tables stored in BigQuery for spatial analysis.

Run solar site prospecting via this MCP Server

`get_solar_resource_data` fetches global horizontal irradiance and direct normal irradiance data for any coordinate on earth. Your Google ADK agent can trigger this tool to assess solar potential and immediately write the raw irradiance values into a Vertex AI pipeline. Before pulling the trigger on an order, the agent runs `list_available_datasets` to check which solar models cover your target region. This keeps your prospecting pipeline automated and grounded in verified satellite observations.

Order and process massive mesoscale climate datasets

`get_mesoscale_climate_data` extracts long-term climate variables for regional wind and solar developers. Google ADK handles the transport layer, letting your agent coordinate the order via `place_data_order` and monitor progress in the background. Once `get_order_status` flags the dataset as ready, the agent pulls down the files using `download_order_data`. The retrieved climate arrays can be dumped directly into Google Cloud Storage for distributed computing runs.

Setup guide

Set up DNV Renewables MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with DNV Renewables tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="DNV Renewables_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DNV Renewables tools via MCP.",
    tools=mcp_tools,
)

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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 Google ADK

Install the ADK, set up your server parameters using `StreamableHttpServerParameters` with your host URL, and pass the resulting toolset to your `LlmAgent`. Your Gemini models can immediately call the wind and solar tools.

Yes, your agent can call `get_wind_resource_data` to fetch localized wind speeds, format the JSON payload, and load it directly into a BigQuery table using native Google Cloud tools.

The agent uses a polling loop that checks `get_order_status` at set intervals. Because Gemini has a massive context window, the agent can retain the entire order history and coordinate details throughout the polling cycle.

Yes, you can use the `tool_names` filter in the `McpToolset` configuration to expose only prospecting tools like `get_solar_resource_data` while hiding ordering tools.

All operations execute inside an ephemeral, zero-trust V8 sandbox on Vinkius. Your site coordinates and order IDs are processed in memory and never written to persistent disks, meeting strict enterprise security compliance.

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