How to Use the NOAA Forecast — US Weather Predictions MCP in Google ADK

Q: How do I connect my Google ADK agent to this server?

Install the SDK with pip install google-adk. Then instantiate McpToolset with your Vinkius server URL. Pass that toolset to your agent's constructor.

Q: Can I limit which NOAA tools my agent can use?

Yes. The McpToolset constructor accepts a toolnames argument. You can pass a list like 'getforecast', 'gethourlyforecast' to expose only those specific tools to your agent.

Connect Google ADK agents to live NOAA weather data. Pull forecasts directly into your Google Cloud workflows.

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Connect NOAA Forecast — US Weather Predictions MCP to Google ADK

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Setup NOAA Forecast — US Weather Predictions with Google ADK

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Load Weather Data into BigQuery

Your ADK agent can call `get_grid_data` to fetch raw temperature, humidity, and precipitation arrays for any location in the US. This gives you direct access to the same data meteorologists use for their models. From there, the agent can write that data directly into a BigQuery table. You can build historical weather datasets, train custom models on Vertex AI, or join weather data with your own business metrics, all within your GCP project.

Analyze Weather Patterns with Gemini

Use an agent running on a long-context Gemini model to analyze nationwide weather trends. The agent can call `get_forecast_discussion` for multiple NWS offices—say, from Denver to Chicago to New York. It gets the full text from each meteorologist. With its large context window, the agent can hold all those discussions in memory to identify the track and intensity of a storm system moving across the country. It’s like having a meteorologist who can read and synthesize reports from a dozen offices at once.

Trigger Cloud Functions with this MCP Server

This isn't just about getting data; it's about acting on it. Your Google ADK agent can monitor a location using `get_hourly_forecast` from this MCP Server. When the forecast shows a high probability of freezing rain, the agent can trigger a Google Cloud Function to alert a logistics team, reroute a fleet, or activate a public safety notification. It connects live weather directly to your cloud infrastructure.

Setup guide

Set up NOAA Forecast — US Weather Predictions 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 NOAA Forecast — US Weather Predictions 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="NOAA Forecast — US Weather Predictions_agent",
    model="gemini-2.0-flash",
    instruction="You have access to NOAA Forecast — US Weather Predictions tools via MCP.",
    tools=mcp_tools,
)

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Common questions about NOAA Forecast — US Weather Predictions MCP in Google ADK

Install the SDK with `pip install google-adk`. Then instantiate `McpToolset` with your Vinkius server URL. Pass that toolset to your agent's constructor.

Yes. The `McpToolset` constructor accepts a `tool_names` argument. You can pass a list like `['get_forecast', 'get_hourly_forecast']` to expose only those specific tools to your agent.

Set up a recurring agent or Cloud Scheduler job. Have it call `get_grid_data` daily for your locations of interest and append the results to a BigQuery table. You'll build a valuable dataset for analysis.

The server processes calls one at a time. For batch analysis, your Google ADK agent should iterate through your list of locations and call a tool like `get_forecast` for each one.

Vinkius acts as a secure proxy. Your agent sends a latitude and longitude, Vinkius forwards it to the public NWS API inside a sandboxed environment, and the response is returned. No location data is stored by Vinkius.

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