How to Use the MeteoSource MCP in Google ADK

Q: Can my Google ADK agent cross-reference MeteoSource data with BigQuery?

Yes, your agent can retrieve weather coordinates using searchweatherplaces and immediately query BigQuery to find matching regional sales data.

Q: Can I use this MCP Server with Gemini's context window?

Yes. You can load months of forecast data from getpointforecast into the Gemini context window without running out of memory.

Feed live MeteoSource weather data directly into your Google ADK pipelines for long-context analysis with Gemini.

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Connect MeteoSource MCP to Google ADK

Create your Vinkius account to connect MeteoSource 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 MeteoSource with Google ADK

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Inject weather forecasts into Google ADK pipelines

The `get_point_forecast` tool brings raw weather data directly into your Google ADK context window. Because Gemini models handle massive token limits, you can feed weeks of hourly forecast data into a single prompt for deep seasonal trend analysis. The ADK handles the raw JSON payload and formats it for immediate consumption by your LLM. You don't need to write custom parsing code to clean up the forecast data before analyzing it.

Map coordinates to BigQuery via Google ADK

The `get_nearest_weather_place` tool resolves raw coordinates from your BigQuery tables into monitored weather stations using your Google ADK setup. Your enterprise agent reads latitude and longitude columns directly from your cloud database, queries the MCP Server, and writes the station data back. This direct integration keeps your data pipelines clean. The agent bridges your cloud data warehouses and live weather tracking without requiring intermediate ETL tools.

Resolve timezone data in Google ADK agents

The `get_place_timezone` tool lets your Google ADK agent align weather forecasts with localized delivery schedules. The agent matches the timezone of a specific place ID to ensure that your logistics planning matches local operational hours. This prevents scheduling mismatches when coordinating global shipments. The Gemini model reads the timezone output directly to calculate arrival windows in the local clock time of the destination.

Setup guide

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

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Common questions about MeteoSource MCP in Google ADK

You can pass a filtered list of tool names to the toolset initialization in Python. This lets you expose only `get_point_forecast` while blocking administrative tools like status checks.

Yes, your agent can retrieve weather coordinates using `search_weather_places` and immediately query BigQuery to find matching regional sales data.

The ADK uses connection pooling to handle multiple requests to `get_point_forecast` concurrently. This keeps your pipeline latency low when processing thousands of locations.

Yes. You can load months of forecast data from `get_point_forecast` into the Gemini context window without running out of memory.

No. Vinkius runs this MCP Server in an ephemeral V8 Isolate sandbox. Your place ID queries are transmitted using secure transit protocols and are never logged or stored on any persistent disk.

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