How to Use the Meteostat MCP in Google ADK

Q: How do I register Meteostat tools in Google ADK?

You initialize an McpToolset with your Vinkius HTTP endpoint. Then, include this toolset in your LlmAgent configuration to make the weather data available to your models.

Q: Can Google ADK limit which Meteostat tools are exposed?

Yes. You can use the toolnames filter in your McpToolset definition to restrict your agent to only the specific tools you need for your weather integration.

Integrate global weather statistics into your Google ADK enterprise workflows.

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Connect Meteostat MCP to Google ADK

Create your Vinkius account to connect Meteostat 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 Meteostat with Google ADK

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Granular weather data for Google ADK

Get historical insights by calling `point_daily` or `point_hourly` directly from your Gemini-powered agent. This allows your cloud-native infrastructure to pull weather context without leaving the execution loop. Your agent can process these results alongside BigQuery data. Use `point_monthly` to aggregate long-term trends for large-scale planning tasks.

Station-level observations via Google ADK

Query specific weather stations using `stations_daily` and `stations_hourly`. The tools provide direct access to historical records, which you can pipe into Vertex AI for further forecasting. Use `stations_meta` to confirm the station status before pulling data. This prevents errors when your agent attempts to query specific WMO or ICAO identifiers.

Automated station discovery

Find the most relevant sensors for your location with `stations_nearby`. Your agent calculates the distance and selects the best station automatically based on geographic proximity. Once identified, use `stations_normals` to pull climate averages. This helps your agent build a complete picture of regional conditions for your enterprise reports.

Setup guide

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

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Common questions about Meteostat MCP in Google ADK

You initialize an `McpToolset` with your Vinkius HTTP endpoint. Then, include this toolset in your `LlmAgent` configuration to make the weather data available to your models.

Yes. You can use the `tool_names` filter in your `McpToolset` definition to restrict your agent to only the specific tools you need for your weather integration.

The server supports both Stdio and HTTP transports natively. You just need to match the transport type in your `StreamableHttpServerParameters` to get the connection running.

Since you are working in a cloud environment, you should process the hourly data in chunks. The `stations_hourly` tool limits results to 30 days per request to keep your memory usage stable.

All data flows through the Vinkius zero-trust sandbox. The server only transmits the requested weather observations, and your endpoint token ensures that only your authorized agent can initiate requests.

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