How to Use the NASA DONKI — Space Weather Intelligence MCP in AutoGen

Q: How do I connect the NASA DONKI — Space Weather Intelligence MCP Server to AutoGen?

Install the autogen-extmcp package. Use mcpservertools with StreamableHttpServerParams pointing to your endpoint URL. Pass the returned tools list into your AssistantAgent constructor.

Q: Do AutoGen agents automatically understand NASA DONKI — Space Weather Intelligence schemas?

Yes. The McpToolAdapter handles the conversion behind the scenes. Your agents read the descriptions and know exactly which parameters to pass for each endpoint.

Deploy AutoGen agent teams to debate and analyze NASA space weather threats.

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Connect NASA DONKI — Space Weather Intelligence MCP to AutoGen

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Setup NASA DONKI — Space Weather Intelligence with AutoGen

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Multi-Agent Analysis via MCP Server

`get_donki_notifications` alerts your AutoGen team to a new solar event. A designated forecasting agent reads the feed and proposes an initial threat level. A separate risk-assessment agent challenges this conclusion by pulling specific data from `get_cme`. Agents debate the severity before waking up human operators. If the forecaster sees a massive ejection, the risk agent might argue that the trajectory misses Earth. They negotiate the final output based on the raw telemetry, giving you a vetted consensus rather than a blind alert.

Resolve Conflicting Solar Metrics

`get_solar_flares` provides the initial electromagnetic burst data, while `get_geomagnetic_storms` tracks the actual impact on Earth's magnetic field. One agent specializes in flare classifications and another monitors Kp indexes. They work together to map cause and effect. You watch them deliberate in real-time. The flare agent flags an X-class event, demanding immediate action. The storm agent checks the Kp index, sees a value of 4, and downgrades the immediate grid threat. The system handles the complex correlation.

Automate Orbital Risk Assessments

`get_interplanetary_shocks` feeds data to an engineering agent tasked with protecting satellite hardware. This agent cross-references the shockwave timing with `get_radiation_belt` and `get_solar_energetic_particles` metrics. It builds a case for putting specific satellites into safe mode. A financial agent reviews the proposal, weighing the cost of downtime against the probability of hardware damage. They'll fight over the margins. You define the rules of engagement, and the agents execute the logic using live NASA data.

Setup guide

Set up NASA DONKI — Space Weather Intelligence MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes NASA DONKI — Space Weather Intelligence tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="NASA DONKI — Space Weather Intelligence_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent NASA DONKI — Space Weather Intelligence data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="NASA DONKI — Space Weather Intelligence_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent NASA DONKI — Space Weather Intelligence data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Install the `autogen-ext[mcp]` package. Use `mcp_server_tools` with `StreamableHttpServerParams` pointing to your endpoint URL. Pass the returned tools list into your `AssistantAgent` constructor.

Yes. The `McpToolAdapter` handles the conversion behind the scenes. Your agents read the descriptions and know exactly which parameters to pass for each endpoint.

They certainly can. You assign the toolset to specific agents within a group chat. A researcher agent can pull the metrics while a critic agent reviews the findings.

The extension supports both standard stdio for local execution and Streamable HTTP for remote connections. You pick the transport that fits your deployment architecture.

The server executes one-way fetches for interplanetary shock and geomagnetic storm parameters. It cannot read your AutoGen conversation history, agent prompts, or the reasoning steps your system generates.

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