How to Use the OpenEI MCP in Google ADK

Q: Can I filter OpenEI data within my Google ADK agent?

You can use the toolnames filter in your toolset definition. This restricts the agent to only the functions you need, keeping your internal execution environment clean.

Query US electricity tariffs with Google ADK for enterprise-grade solar analysis and energy cost forecasting.

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Connect OpenEI MCP to Google ADK

Create your Vinkius account to connect OpenEI to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup OpenEI with Google ADK

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Direct utility rate integration

Connect your Google ADK agent to the grid by using `get_utility_rates`. It fetches sector-specific pricing data that your agents can push straight into your BigQuery datasets for long-term analysis. This pipeline handles the data ingestion for you. No more manual CSV uploads or cleaning messy tariff PDFs.

Location-based energy intelligence

Invoke `get_rates_by_address` to resolve a street address into specific utility tariffs. Your Google ADK agent uses this to provide immediate, location-aware energy insights for any site in the US. It keeps your proposal software updated without human intervention. The agent does the heavy lifting, connecting geographic inputs to raw pricing data.

Utility research at scale

Use `list_utilities` to scan the OpenEI database for specific service territories. Your Google ADK agent can iterate through these results to build a comprehensive map of utility offerings across your project regions. This is the fastest way to understand market coverage. Your agent identifies the right players, giving you a clear view of the energy landscape.

Setup guide

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

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Common questions about OpenEI MCP in Google ADK

Initialize an McpToolset with your server URL and pass it to your LlmAgent. It integrates with your existing Google Cloud setup using Stdio or HTTP transports.

They can, provided you respect the underlying API rate limits. The tools are designed for programmatic access, making them perfect for large-scale energy modeling.

You can use the tool_names filter in your toolset definition. This restricts the agent to only the functions you need, keeping your internal execution environment clean.

The server pulls data directly from the OpenEI database. When a utility updates its tariff, your agent retrieves the new values on its next query.

The server is ephemeral and does not retain your location data. Once the agent retrieves the requested tariff for your address, the connection closes and no history is saved.

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