How to Use the INMET (Apitempo - Meteorologia) MCP in Google ADK

Q: Can Gemini models use INMET (Apitempo - Meteorologia) tools via Google ADK?

Yes, Gemini models natively execute tools like getallforecasts when connected through the ADK. The framework translates the JSON schemas into model-ready declarations automatically.

Q: How do I map INMET (Apitempo - Meteorologia) data directly to BigQuery using Google ADK?

Use the ADK to fetch daily telemetry via getstationdatadaily. Your Python runtime can then stream this structured JSON payload directly into your BigQuery tables for warehousing.

Q: Can I limit which INMET (Apitempo - Meteorologia) endpoints are exposed to Google ADK?

Yes, specify a list of allowed tools in the toolnames parameter of your McpToolset. This lets you isolate access to specific resources like getsatelliteimages while hiding the rest.

Feed official Brazilian meteorological telemetry directly into your Google ADK enterprise pipelines and Gemini long-context models.

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Connect INMET (Apitempo - Meteorologia) MCP to Google ADK

Create your Vinkius account to connect INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) with Google ADK

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Load historical weather data into Gemini long-context windows

The `get_meteorological_data_by_date` tool lets you pull decades of historical weather readings directly into Gemini's million-token context window. This allows your Google ADK agent to perform deep temporal analysis across millions of data points without hitting token limits. Instead of chunking your queries, you can feed entire seasons of hourly station outputs from `get_station_data_hourly` straight to the model. The agent analyzes long-term trends, spots microclimate shifts, and outputs clean structured summaries instantly.

Feed BigQuery pipelines with real-time MCP Server telemetry

The `get_station_data_daily` tool collects official government sensor data that you can pipe directly into your BigQuery data warehouse. By combining Google ADK with this MCP Server, your enterprise agents can cross-reference live weather telemetry with your internal logistics data. This setup lets you automate complex supply chain decisions based on current soil moisture or precipitation levels. Your agent runs `get_meteorological_data_by_region` to check regional conditions, then updates your BigQuery tables to adjust shipping schedules.

Restrict Gemini tool access using Google ADK tool filters

The `list_stations` tool maps out all active meteorological sensors across Brazil. To prevent your agent from running unauthorized queries, you can use the ADK's built-in tool filters to limit access to specific endpoints. If your model only needs current outlooks, you can configure the toolset to expose `get_forecast_by_city` while blocking historical tools. This ensures your agent stays focused on the task and doesn't waste compute on unnecessary API calls.

Setup guide

Set up INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) 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="INMET (Apitempo - Meteorologia)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to INMET (Apitempo - Meteorologia) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about INMET (Apitempo - Meteorologia) MCP in Google ADK

Yes, Gemini models natively execute tools like get_all_forecasts when connected through the ADK. The framework translates the JSON schemas into model-ready declarations automatically.

Use the ADK to fetch daily telemetry via get_station_data_daily. Your Python runtime can then stream this structured JSON payload directly into your BigQuery tables for warehousing.

Yes, specify a list of allowed tools in the tool_names parameter of your McpToolset. This lets you isolate access to specific resources like get_satellite_images while hiding the rest.

Yes, Vinkius hosts this server on secure HTTP endpoints. You connect your ADK agent using StreamableHttpServerParameters to establish a persistent, low-latency connection.

Your weather queries are processed inside ephemeral, zero-trust V8 isolates. The raw sensor data and municipal search queries are never logged, stored, or used for model training.

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