How to Use the EIA Full Access — U.S. Energy Intelligence MCP in OpenAI Agents SDK

Run production-grade energy market agents with OpenAI Agents SDK and the EIA Full Access MCP Server.

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Connect EIA Full Access — U.S. Energy Intelligence MCP to OpenAI Agents SDK

Create your Vinkius account to connect EIA Full Access — U.S. Energy Intelligence to OpenAI Agents SDK 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 EIA Full Access — U.S. Energy Intelligence with OpenAI Agents SDK

Ask AI about this MCP

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Claude

Perplexity

Map fuel supply chains with custom guardrails

`get_crude_production` feeds raw U.S. oil volume data straight to your OpenAI Agents SDK workflow. The tool pulls weekly and monthly extraction metrics directly from the federal registry, letting your agent run real-time supply analysis without manual data scraping. You set the execution limits in your Python code. If the agent tries to pull international metrics via `get_international_data` during a domestic-only run, your safety guardrails intercept the call before it hits the network.

Track power grid loads with zero-config MCP Server tools

`get_grid_demand` pulls hourly and daily electricity consumption across major regional transmission organizations. Your OpenAI agent automatically discovers this tool at runtime, parsing grid stress signals without custom API connectors or hardcoded schemas. Combine this with `get_nuclear_outages` to detect immediate supply constraints. The model processes the raw outages and regional demand spikes side-by-side, giving you a clear picture of grid vulnerability during peak hours.

Forecast short-term pricing in your OpenAI agent network

`get_short_term_outlook` delivers eighteen-month pricing and supply projections directly into your multi-agent architecture using this MCP server. By assigning one specialized agent to macro forecasts and another to spot markets, you can run automated scenario tests. The agents hand off data packages between each other using native SDK protocols. A trading agent receives the outlook, checks `get_petroleum_prices` for prompt-month contracts, and flags deviations from the official federal baseline.

Setup guide

Set up EIA Full Access — U.S. Energy Intelligence MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all EIA Full Access — U.S. Energy Intelligence tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives EIA Full Access — U.S. Energy Intelligence tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate EIA Full Access — U.S. Energy Intelligence tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="EIA Full Access — U.S. Energy Intelligence Agent",
            instructions="You have access to EIA Full Access — U.S. Energy Intelligence tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about EIA Full Access — U.S. Energy Intelligence MCP in OpenAI Agents SDK

Install the SDK via pip and initialize the MCP server using the streamable HTTP transport class. You pass the Vinkius endpoint token in the headers, and the Agent constructor automatically registers all thirty-four energy tools. No manual schema mapping is required.

Yes, the SDK handles rate limiting through native retry logic and backoff configurations. Since Vinkius manages the underlying server connection, your Python code only needs to catch standard API exceptions if the federal endpoint experiences temporary downtime.

You can filter the tools during the initialization step before passing them to the Agent constructor. By exposing only specific tools like `get_natgas_storage` or `get_petroleum_stocks`, you keep the model's context window clean and prevent unnecessary token usage.

The server runs inside an async context manager within your Python environment, allowing multiple agents to query different endpoints concurrently. You can pull `get_coal_prices` and `get_electricity_prices` simultaneously without blocking your main execution thread. This keeps high-frequency simulations highly responsive.

Vinkius runs the server inside a zero-trust V8 isolate sandbox, meaning your API credentials never touch the client-side code directly. Your queries for petroleum stocks, gas storage, and grid demand are processed in ephemeral environments that wipe all session data immediately after execution.

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