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

Q: How do I connect NASA DONKI — Space Weather Intelligence to LlamaIndex as an MCP Server?

Run pip install llama-index-tools-mcp in your environment. Set up a BasicMCPClient with the server URL, wrap it in McpToolSpec, and call await mcptoolspec.totoollistasync(). Pass those tools directly to your FunctionAgent.

Q: Does LlamaIndex support filtering NASA DONKI — Space Weather Intelligence tools?

Yes. You pass an allowedtools list during setup. If you only want your agent checking flares and storms, you exclude the radiation and shock endpoints.

Q: How do I access raw MCP tool data in my queries?

Set includeresources=True when configuring the client. This allows the agent to read the raw data blobs alongside the standard tool execution paths.

Index live NASA space weather telemetry into searchable LlamaIndex vector stores.

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Connect NASA DONKI — Space Weather Intelligence MCP to LlamaIndex

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

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Build RAG Applications with LlamaIndex MCP Server

`get_donki_notifications` feeds real-time space weather alerts directly into your document index. LlamaIndex takes these raw notifications and converts them into searchable embeddings. You query the vector store to spot historical patterns instead of hammering the API. Combine this with internal operating procedures. When you ask your agent about satellite safe-modes, it searches the indexed `get_cme` data to ground its answer in actual coronal mass ejection trajectories. Hallucinations drop to zero because the context window relies on verified NASA metrics.

Ground Queries in Hard Telemetry

`get_solar_flares` returns precise classification data, including begin, peak, and end times for C, M, and X-class events. Your RAG setup ingests these timestamps alongside `get_geomagnetic_storms` severity indexes. The agent cross-references Kp values against the flare classifications to answer complex questions. You don't have to guess about past solar cycles. The vector store holds the exact parameters of every storm above Kp=7. Users just ask natural language questions, and the system retrieves the specific interplanetary event that caused the disruption.

Search Orbital Threat Histories

`get_interplanetary_shocks` provides the physical disturbance data in the solar wind. LlamaIndex pairs this with `get_radiation_belt` and `get_solar_energetic_particles` outputs to create a complete profile of orbital hazards. Every energized Van Allen belt event becomes a searchable node. Engineers query the agent to see how previous shockwaves affected radiation levels. The function agent pulls the relevant tools, runs the queries, and indexes the results on the fly. You get answers backed by hard numbers.

Setup guide

Set up NASA DONKI — Space Weather Intelligence MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all NASA DONKI — Space Weather Intelligence MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to NASA DONKI — Space Weather Intelligence tools.",
)
response = await agent.run("List recent NASA DONKI — Space Weather Intelligence data")

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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 LlamaIndex

Run `pip install llama-index-tools-mcp` in your environment. Set up a `BasicMCPClient` with the server URL, wrap it in `McpToolSpec`, and call `await mcp_tool_spec.to_tool_list_async()`. Pass those tools directly to your `FunctionAgent`.

That is the primary use case. You query the endpoints and ingest the JSON responses as documents. The framework embeddings turn those space weather events into a semantic knowledge base.

Yes. You pass an `allowed_tools` list during setup. If you only want your agent checking flares and storms, you exclude the radiation and shock endpoints.

Set `include_resources=True` when configuring the client. This allows the agent to read the raw data blobs alongside the standard tool execution paths.

This specific integration only fetches external solar energetic particle and radiation belt data from NASA. It possesses zero access to your internal LlamaIndex documents, vector databases, or local file systems.

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