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

Q: How do I connect NASA DONKI — Space Weather Intelligence to LangChain?

Install langchain-mcp-adapters and langgraph. Initialize the server using MultiServerMCPClient with your HTTP transport URL. Call client.gettools() and pass the resulting list into your agent creation function.

Build multi-step space weather pipelines with LangChain agents connected to live NASA telemetry.

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Connect NASA DONKI — Space Weather Intelligence MCP to LangChain

Create your Vinkius account to connect NASA DONKI — Space Weather Intelligence to LangChain 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 LangChain

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Chain NASA Telemetry via LangChain MCP Server

`get_donki_notifications` acts as the trigger for your LangChain agent. When a notification hits, the agent parses the event type and decides which specific endpoint to query next. It pulls X-class flare data using `get_solar_flares` and feeds those timestamps directly into the next tool call. You build a ReAct loop that correlates these events. If the flare data shows a mass ejection, the agent automatically runs `get_cme` to check the trajectory. LangSmith traces the exact token usage and latency for every API call to the NASA database.

Correlate Interplanetary Shocks and Storms

`get_interplanetary_shocks` delivers the raw disturbance metrics preceding major space weather events. Your agent reads this solar wind data and passes it down the chain. The next step queries `get_geomagnetic_storms` to map the Kp index against the initial shockwave. This setup removes manual correlation work. The agent evaluates if the Kp index exceeds 7, triggering a secondary alert chain for power grid operators. You dictate exactly how data flows from detection to warning.

Track Orbital Threats Automatically

`get_solar_energetic_particles` pulls the exact proton flux numbers threatening orbital assets. A custom chain takes this SEP data and checks it against historical baselines. The agent then pulls `get_radiation_belt` metrics to see if the Van Allen belts are energized. You get a hard, actionable threat assessment. Instead of writing custom API wrappers for NASA, you drop these MCP tools into your existing agent architecture. The client handles the HTTP transport while you focus on the logic.

Setup guide

Set up NASA DONKI — Space Weather Intelligence MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes NASA DONKI — Space Weather Intelligence tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "nasa-donki-space-weather-intelligence-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent NASA DONKI — Space Weather Intelligence transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Install `langchain-mcp-adapters` and `langgraph`. Initialize the server using `MultiServerMCPClient` with your HTTP transport URL. Call `client.get_tools()` and pass the resulting list into your agent creation function.

Yes. You build a persistent ReAct loop that checks the notification feed at set intervals. Use `client.session()` to maintain the context history across multiple polling cycles.

You set the initial condition. Most developers use the notification endpoint as a cron-triggered entry point. Once an event registers, the chain decides which specific metric to fetch next.

LangSmith handles that out of the box. Every call to the MCP server logs the exact execution time, token consumption, and the raw inputs and outputs.

The server only retrieves public coronal mass ejection, flare, and storm metrics from NASA. It never reads your local environment variables or sees the internal routing logic of your LangGraph application.

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