How to Use the Enverus Energy Intelligence MCP in OpenAI Agents SDK

Plug Enverus Energy Intelligence directly into your OpenAI Agents SDK pipeline for real-time rig tracking and production telemetry.

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Connect Enverus Energy Intelligence MCP to OpenAI Agents SDK

Create your Vinkius account to connect Enverus 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 Enverus Energy Intelligence with OpenAI Agents SDK

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Real-time rig and well monitoring

Your agent consumes live drilling data by calling `list_active_drilling_rigs` and `get_rig_technical_details`. It pulls the exact specifications of any rig in the field, moving past guesswork into hard engineering metrics. Production telemetry flows into your agent via `get_well_production_telemetry`. You get the historical performance logs needed to validate asset viability before your agent triggers a downstream decision.

Automated energy asset auditing

Run `quick_energy_asset_audit` to get a snapshot of basin activity without manual database queries. Your agent evaluates rig counts and well statuses in seconds, keeping your internal models current with daily market shifts. Use `list_basin_specific_activity` to filter data by geological region. This narrows the scope for your OpenAI Agents SDK workflow, ensuring your agent only processes relevant data for specific investment targets.

Market intelligence and M&A tracking

Feed `get_energy_market_intelligence_summary` into your agent to track macro trends. It provides the high-level context your system needs to interpret specific drilling movements. Identify deal flow by invoking `list_energy_m_and_a_deals`. Your agent flags new transactions, allowing you to react to asset changes across the energy sector as soon as they hit the database.

Setup guide

Set up Enverus 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 Enverus Energy Intelligence tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Enverus 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 Enverus 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="Enverus Energy Intelligence Agent",
            instructions="You have access to Enverus 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 Enverus Energy Intelligence MCP in OpenAI Agents SDK

You initialize the server via the MCPServerStreamableHttp class and register it in your agent constructor. This setup allows your agent to auto-discover all 10 available tools without extra configuration.

Yes, the integration supports cached tool lists for performance. Your agent fetches rig and production data through the HTTP transport, which handles the load required for production-grade analysis.

The server manages your connection via an endpoint token. Your agent validates its authorization status automatically using the `get_enverus_api_metadata` tool before executing any data requests.

Your agent receives the raw response payload defined by the tool. You should implement standard error handling within your agent logic to manage any unexpected API output gracefully.

Your data remains isolated within the Vinkius sandbox. The server only transmits the specific rig, well, or M&A information requested by your agent, keeping your internal analytical models separate from the external API traffic.

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