How to Use the Corrently Regional Green Index MCP in Google ADK

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Connect Corrently Regional Green Index MCP to Google ADK

Create your Vinkius account to connect Corrently Regional Green Index 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 Corrently Regional Green Index with Google ADK

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Query market prices in Google ADK

Your agent uses `get_energy_market_data` to pull the latest pricing from the grid. This info flows straight into your Google Cloud infrastructure for analysis. Since you are building on Google ADK, you can pipe this price data directly into BigQuery. It makes tracking your energy spend simple and automated.

Forecast green energy for Google ADK agents

Call `get_regional_green_index` to see when your local grid is using the most renewable energy. Your agent reads this forecast to decide when to scale your workload. This works perfectly with the long-context windows in Gemini. You can feed weeks of grid data into the model for high-level pattern recognition.

Filter energy tools in Google ADK

Use the tool_names filter to restrict which operations your agent can access. You might only expose `get_regional_green_index` to keep your agent focused on sustainability goals. This keeps your Google ADK agent logic clean and prevents unnecessary API calls. It gives you tight control over how the agent interacts with external energy data.

Setup guide

Set up Corrently Regional Green Index 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 Corrently Regional Green Index 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="Corrently Regional Green Index_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Corrently Regional Green Index tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Corrently Regional Green Index MCP in Google ADK

Use the McpToolset class with your server URL. Pass this toolset into your LlmAgent definition to register the tools.

The server supports both Stdio and HTTP transports. You pick the transport that fits your Google Cloud deployment architecture.

Gemini models handle large context inputs easily. You can pull massive amounts of regional index data and let the agent summarize the best times to run processes.

The agent receives the tool output as a structured response. You can then write this directly to your connected BigQuery datasets for long-term storage.

All traffic is encrypted over HTTPS. Your Google ADK agent only processes public grid forecasts, so no proprietary internal data is ever exposed to the MCP server.

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