How to Use the Tomorrow.io MCP in Google ADK

Q: Can I use Tomorrow.io to assess driving risks with the Google ADK?

Yes. You can call getroadweatherrisk to retrieve assessments for driving and road hazards, which your enterprise agent can then factor into logistics planning.

Q: Does tomorrowio-mcp support custom time windows with the Google ADK?

Absolutely. Use getcustomtimelines to query weather data for precise, non-standard intervals or date ranges that fit your BigQuery schema.

Build enterprise weather agents with the Google ADK.

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Connect Tomorrow.io MCP to Google ADK

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

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MCP Server: Analyzing Location Data for Google ADK

If your agent needs to act on location, use `get_realtime_weather` to get current conditions using any identifier—city name, zip code, or latitude/longitude. It’s the starting point for most operational tasks. For risk analysis, you can feed the data into BigQuery and process it with tools like `get_road_weather_risk`. This gives your agent context on immediate hazards affecting infrastructure.

Predicting Weather Trends with Google ADK

You’ll get deep forecasting using both `get_daily_forecast` and the more granular `get_hourly_forecast`. These tools return predicted totals and extremes for planning purposes. Need to look back? The agent can query actual recorded history via `get_historical_weather`. It lets you build complex models that compare today's prediction against past performance.

Handling Specialized Climate Risks with Google ADK

The server supports highly specialized climate assessments. Check wildfire risk with `get_wildfire_risk` or get a precise, moment-by-moment view of rainfall using `get_minutely_precipitation`. This goes beyond standard weather reports. Furthermore, you can manage seasonal planning by calling `get_pollen_forecast` to track daily pollen counts for your enterprise users.

Setup guide

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

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Common questions about Tomorrow.io MCP in Google ADK

The MCP Server exposes its tools, letting your LlmAgent execute functions like `get_air_quality_index` natively within the Gemini ecosystem. Your agent can manage tool calls just like it manages standard data queries in BigQuery.

The server provides a massive range, including minute-by-minute precipitation nowcasts, daily forecasts (`get_daily_forecast`), and detailed real-time conditions across global locations.

Yes. You can call `get_road_weather_risk` to retrieve assessments for driving and road hazards, which your enterprise agent can then factor into logistics planning.

Absolutely. Use `get_custom_timelines` to query weather data for precise, non-standard intervals or date ranges that fit your BigQuery schema.

The MCP Server handles critical environmental data including air quality index readings, pollen counts, and historical weather observations.

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