How to Use the Open-Meteo Climate & Ensemble MCP in Google ADK

Q: How do I use the Open-Meteo Climate & Ensemble tools with a Google ADK agent?

You define an McpToolset with the server's URL and pass it into your LlmAgent's tool list. The agent can then call tools like getensembleforecast as if they were native Python functions.

Connect your Google ADK agent to IPCC climate data. Analyze global projections directly from your BigQuery tables.

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Connect Open-Meteo Climate & Ensemble MCP to Google ADK

Create your Vinkius account to connect Open-Meteo Climate & Ensemble 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 Open-Meteo Climate & Ensemble with Google ADK

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Feed Gemini with CMIP6 Climate Data

The `get_climate_projection` tool from this MCP server connects your agent to IPCC climate projections through 2100. Your Gemini-powered agent can now reason about long-term climate impacts for specific locations, pulling data on demand. The real power here is combining this with Google ADK's long-context capabilities. Your agent can hold an entire climate report in its context, then use `get_climate_projection` to verify or enrich its analysis with fresh, location-specific data from the CMIP6 models.

Integrate Climate Risk into Vertex AI

`get_ensemble_forecast` delivers probabilistic forecasts from multiple climate models. This isn't just one possible future; it's a weighted range of outcomes. It's the right data for building sophisticated risk models. With Google ADK, your agent can pull this ensemble data and pipe it directly into a Vertex AI pipeline. You can trigger training runs for custom risk models or use the data from this MCP to score assets in your Google Cloud databases. It bridges the gap between raw climate data and your enterprise AI workflow.

Query Temperature Trends from your Google ADK Agent

Use the `get_climate_temperature_trend` tool to give your agent the ability to check long-term temperature trends for any coordinates. This is the fundamental data point for tracking warming and reporting on ESG goals. You can create a simple `McpToolset` pointing to the server and your `LlmAgent` is ready to go. Filter the tools with `tool_names` if you only want to expose temperature trends, giving you fine-grained control over your agent's capabilities.

Setup guide

Set up Open-Meteo Climate & Ensemble 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 Open-Meteo Climate & Ensemble 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="Open-Meteo Climate & Ensemble_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Open-Meteo Climate & Ensemble tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Open-Meteo Climate & Ensemble MCP in Google ADK

You define an `McpToolset` with the server's URL and pass it into your `LlmAgent`'s tool list. The agent can then call tools like `get_ensemble_forecast` as if they were native Python functions.

Yes, that's a primary use case. Your Google ADK agent can query locations from a BigQuery table, call the MCP server to get climate projections for each one, and then write the results back to a new table.

It's built for it. The tools provide data from IPCC-standard CMIP6 models, the gold standard for climate reporting. A Gemini agent using this MCP server can automate the data gathering for your entire ESG report.

Gemini's large context window allows it to analyze complex climate policy documents. Your agent can then use these tools to fetch real-time, location-specific climate model data to ground its analysis in hard numbers.

The only data sent to the server is the set of latitude and longitude coordinates for the location you're analyzing. This request is handled by a Vinkius ephemeral instance that is destroyed after processing. Your Google Cloud identity and the agent's reasoning process are never exposed.

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