How to Use the NASA DONKI — Space Weather Intelligence MCP in OpenAI Agents SDK

Q: How do I connect NASA DONKI — Space Weather Intelligence to the OpenAI Agents SDK?

Install openai-agents via pip. Create an MCPServerStreamableHttp instance with your server URL, then pass it in the mcpservers list when initializing your Agent. Set cacheToolsList=True to speed up tool discovery.

Q: Can the OpenAI Agents SDK predict aurora visibility using NASA DONKI — Space Weather Intelligence?

Yes. Your agent queries getgeomagneticstorms to check current Kp indices. High Kp values indicate severe storms, which push aurora visibility to lower latitudes.

Q: Does the OpenAI Agents SDK allow NASA DONKI — Space Weather Intelligence to track satellite radiation risks?

It tracks the environmental factors perfectly. You call getradiationbelt to monitor Van Allen belt energization and getsolarenergeticparticles to check for hardware-damaging SEP events.

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Connect NASA DONKI — Space Weather Intelligence MCP to OpenAI Agents SDK

Create your Vinkius account to connect NASA DONKI — Space Weather 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 NASA DONKI — Space Weather Intelligence with OpenAI Agents SDK

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Track extreme solar events via MCP Server

The `get_solar_flares` and `get_cme` tools feed raw telemetry directly into your OpenAI agent. You get exact begin, peak, and end times for C-class to X-class flares alongside coronal mass ejection vectors. Your agents calculate arrival times for incoming plasma based on these readings. If an X-class flare triggers a radio blackout alert, the OpenAI Agents SDK guardrails ensure the agent halts non-essential API calls until the storm passes.

Monitor grid-threatening geomagnetic storms

The `get_geomagnetic_storms` tool pulls Kp index readings from NASA into your Python environment. Storms hitting Kp=7 or Kp=9 pose massive risks to low-latitude power grids and orbital satellites. You write specialized agents to watch these thresholds. One agent tracks the Kp index, while another queries `get_interplanetary_shocks` to confirm CME-driven disturbances before they hit Earth's magnetosphere.

Audit space weather alerts with full tracing

The `get_donki_notifications` tool delivers a unified feed of radiation events, shocks, and flares. Your agent ingests these alerts to generate daily hazard reports for satellite operators. Because you built this with the OpenAI framework, every single NASA API call shows up in your dashboard. You see exactly when the agent checked `get_solar_energetic_particles` and how it decided to warn about astronaut radiation risks.

Setup guide

Set up NASA DONKI — Space Weather 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 NASA DONKI — Space Weather Intelligence tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives NASA DONKI — Space Weather 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 NASA DONKI — Space Weather 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="NASA DONKI — Space Weather Intelligence Agent",
            instructions="You have access to NASA DONKI — Space Weather Intelligence 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 NASA. 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 NASA DONKI — Space Weather Intelligence MCP in OpenAI Agents SDK

Install `openai-agents` via pip. Create an `MCPServerStreamableHttp` instance with your server URL, then pass it in the `mcp_servers` list when initializing your Agent. Set `cacheToolsList=True` to speed up tool discovery.

Yes. Your agent queries `get_geomagnetic_storms` to check current Kp indices. High Kp values indicate severe storms, which push aurora visibility to lower latitudes.

The SDK includes built-in guardrails. You define strict execution rules for your agent, preventing it from triggering automated system shutdowns based on a single `get_solar_flares` reading without human approval.

It tracks the environmental factors perfectly. You call `get_radiation_belt` to monitor Van Allen belt energization and `get_solar_energetic_particles` to check for hardware-damaging SEP events.

This integration only pulls public space weather metrics like CME vectors and Kp indices. Your proprietary agent logic and internal satellite operational thresholds never leave your secure OpenAI dashboard environment.

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