How to Use the Ember Climate MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent pull historical Ember Climate data for multiple countries at once?

Yes, the MCP server provides getgenerationmultientity. Your agent passes a comma-separated list of country codes to grab the entire dataset in one shot. It cuts down on rate-limiting risks.

Q: What is the best way to find valid filter parameters for these tools?

Run getapioptions first. Your agent can read the available datasets, temporal resolutions, and entity codes directly from the source. It prevents hallucinated variables in your production pipelines.

Connect Ember Climate to OpenAI Agents SDK to route global grid data straight into production trading workflows.

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Connect Ember Climate MCP to OpenAI Agents SDK

Create your Vinkius account to connect Ember Climate 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 Ember Climate with OpenAI Agents SDK

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Query global grids with OpenAI Agents SDK

`get_generation_multi_entity` pulls simultaneous electricity generation data across multiple countries in a single request. Your agent hits this endpoint to grab the coal-to-solar mix for BRICS nations without firing off sequential API calls. This matters when your production system needs fast answers before a trading window closes. OpenAI's built-in guardrails validate the inputs before the request even leaves your server. If an agent tries to pass an invalid country code, the framework catches it early. You can check valid codes first using `get_api_options`.

Track carbon intensity metrics

`get_carbon_intensity_monthly` returns the emissions intensity of regional grids over time. Your agent pulls this to build compliance reports for ESG funds. It shows exactly how dirty or clean a specific market is during seasonal peak loads. Handoffs make this powerful. One specialized agent fetches the baseline data using `get_power_sector_emissions_yearly`. It then passes the raw numbers to an analysis agent that calculates portfolio risk. The entire chain gets logged in your OpenAI tracing dashboard.

Model renewable capacity growth

`get_installed_capacity_monthly` tracks the physical deployment of wind and solar infrastructure across different nations. Your agent uses this to measure actual grid readiness versus political promises. It gives you raw megawatts, not press releases. Pair this with `get_electricity_demand_monthly` to spot curtailment risks. When installed capacity spikes but demand flatlines, grids dump excess power. Your system can flag these anomalies automatically.

Setup guide

Set up Ember Climate 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 Ember Climate tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Ember Climate tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Ember Climate 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="Ember Climate Agent",
            instructions="You have access to Ember Climate tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ember Climate. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Ember Climate MCP in OpenAI Agents SDK

Setup requires the openai-agents package. You instantiate MCPServerStreamableHttp with your endpoint URL and pass it to the Agent constructor in your Python script. Set cacheToolsList=True to speed up tool discovery.

Yes, the MCP server provides get_generation_multi_entity. Your agent passes a comma-separated list of country codes to grab the entire dataset in one shot. It cuts down on rate-limiting risks.

The framework enforces strict input constraints before execution. If your agent invents a fake energy source for get_electricity_generation_monthly, the guardrails block the call. You avoid burning API quotas on bad requests.

Run get_api_options first. Your agent can read the available datasets, temporal resolutions, and entity codes directly from the source. It prevents hallucinated variables in your production pipelines.

No, the architecture isolates your logic. The server only sees outbound requests for public grid data like TWh generation or CO2 emissions. Your internal portfolio weights and agent instructions never leave your infrastructure.

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