How to Use the Weatherbit MCP in Google ADK

Q: Can I use Weatherbit data with Google ADK?

Yes. You can query current conditions using getcurrentweather via the MCP Server. This ensures your Gemini models have access to reliable, geographically specific weather metrics for decision-making.

Q: What types of forecasts are available with Weatherbit and Google ADK?

You can retrieve daily forecasts using getforecastdailybycity (up to 16 days) or get granular hourly data via getforecasthourlybycity. The MCP Server handles both time scales.

Q: Does Weatherbit support analyzing past weather with Google ADK?

It does. Use the gethistoricalweather tool to retrieve metrics like temperature, wind, and humidity for any coordinate set dating back 30 years. This is ideal for trend analysis.

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Create your Vinkius account to connect Weatherbit 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 Weatherbit with Google ADK

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Get real-time weather insights.

The `get_current_weather` tool pulls immediate conditions—temperature, feels like, cloud cover, and UV index—for any location. This helps your Gemini models ground their reasoning with accurate, up-to-the-minute data. The agent also supports using `get_current_weather_by_city`, giving you the core metrics even if you only have a city name to work with.

Analyze air quality and alerts.

The `get_air_quality` tool provides crucial environmental data, listing AQI scores and pollutant concentrations like SO2 and NO2. This allows your agent to build safety logic into complex workflows. When planning for risk mitigation, use `get_weather_alerts` to pull real-time alert types, severities, and affected areas.

Run historical data queries.

The `get_historical_weather` tool lets your agent query temperature, precipitation, and wind metrics for dates stretching back 30 years. You can build predictive models using this deep dataset. If you only need daily planning data, the `get_forecast_daily` function retrieves high/low temperatures, conditions, and sunrise/sunset times.

Setup guide

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

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Common questions about Weatherbit MCP in Google ADK

The MCP Server exposes its 10 tools to the LlmAgent framework. Your agent can then call these tools, allowing you to pull weather data and pass it directly into BigQuery or Vertex AI for further processing.

Yes. You can query current conditions using `get_current_weather` via the MCP Server. This ensures your Gemini models have access to reliable, geographically specific weather metrics for decision-making.

You can retrieve daily forecasts using `get_forecast_daily_by_city` (up to 16 days) or get granular hourly data via `get_forecast_hourly_by_city`. The MCP Server handles both time scales.

It does. Use the `get_historical_weather` tool to retrieve metrics like temperature, wind, and humidity for any coordinate set dating back 30 years. This is ideal for trend analysis.

This MCP Server primarily touches weather parameters: temperatures, precipitation probability, air quality indices (PM2.5), wind speed/direction, and alert severity levels.

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