How to Use the Tomorrow.io Extended MCP in Google ADK

Q: How should I use Tomorrow.io Extended with the Google ADK for predictive modeling?

You combine getweatherforecast data with your internal operational metrics in BigQuery. The agent can then run complex predictions, which is where its value really shines.

Build enterprise agents using the Google ADK that act on Tomorrow.io Extended data.

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Connect Tomorrow.io Extended MCP to Google ADK

Create your Vinkius account to connect Tomorrow.io Extended 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 Tomorrow.io Extended with Google ADK

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Get air quality measurements with `get_air_quality`

Your Gemini agent calls `get_air_quality` to pull pollutant levels. You get specific readings for things like ozone or particulate matter, which is crucial for health planning. This data flows naturally into BigQuery. Your agents can then query and analyze this air quality information alongside your existing operational datasets.

Analyze historical weather trends via `get_recent_history`

Need to compare today's weather to last month? Use `get_recent_history`. This tool provides detailed records of past conditions, letting you spot seasonal patterns. Because it integrates with Google Cloud infrastructure, you can run long-context reasoning on this historical data using Gemini models.

Plan for alerts and risks using `list_weather_alerts`

Before deploying anything outdoors, ask the agent to list weather alerts. The `list_weather_alerts` tool pulls warnings in real-time. This allows your enterprise agents to automatically pause operations or notify staff based on active dangers detected by the MCP Server.

Setup guide

Set up Tomorrow.io Extended 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 Tomorrow.io Extended 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="Tomorrow.io Extended_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Tomorrow.io Extended tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Tomorrow.io Extended MCP in Google ADK

The agent framework is built for this. It acts as a bridge, allowing your agents to retrieve weather and air quality metrics from the MCP Server and write them directly into structured tables in BigQuery.

Yes. Gemini models support long-context reasoning, meaning they can process massive amounts of weather history or detailed air quality reports from the MCP Server without losing context.

You combine `get_weather_forecast` data with your internal operational metrics in BigQuery. The agent can then run complex predictions, which is where its value really shines.

It supports both Stdio and HTTP transports. This means you can plug it into headless CI/CD pipelines or use it in standard web service integrations easily.

The server provides atmospheric and environmental measurements, including temperature ranges, wind speeds, and detailed air quality indices (AQI).

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