What is the difference between DONKI and SWPC?

DONKI is NASA's historical database of space weather events with detailed analysis. SWPC (Space Weather Prediction Center, under NOAA) focuses on real-time monitoring and forecasting. They complement each other.

What happens during a Geomagnetic Storm?

Geomagnetic storms, triggered by solar activity, can cause auroras, disrupt satellite communications, affect GPS accuracy, and in extreme cases, induce currents that trip electrical grids.

How long does it take for a CME to reach Earth?

Coronal Mass Ejections typically take 1 to 5 days to reach Earth, depending on their speed, which can range from 250 km/s to over 3,000 km/s.

NASA DONKI — Space Weather Intelligence MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: NASA DONKI — Space Weather Intelligence
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect NASA DONKI — Space Weather Intelligence through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to NASA DONKI — Space Weather Intelligence "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in NASA DONKI — Space Weather Intelligence?"
    )
    print(result.data)

asyncio.run(main())

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About NASA DONKI — Space Weather Intelligence MCP Server

DONKI (Database Of Notifications, Knowledge, Information) is NASA's comprehensive space weather database.

Pydantic AI validates every NASA DONKI — Space Weather Intelligence tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

7 Event Types

☀️ CME — Coronal Mass Ejections
🔥 Solar Flares — C, M, X class
🧲 Geomagnetic Storms — Kp ≥ 4
💥 Interplanetary Shocks
⚡ Solar Energetic Particles
🛡️ Radiation Belt Events
📡 All Notifications (unified feed)

The NASA DONKI — Space Weather Intelligence MCP Server exposes 7 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect NASA DONKI — Space Weather Intelligence to Pydantic AI via MCP

Follow these steps to integrate the NASA DONKI — Space Weather Intelligence MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from NASA DONKI — Space Weather Intelligence with type-safe schemas

Why Use Pydantic AI with the NASA DONKI — Space Weather Intelligence MCP Server

Pydantic AI provides unique advantages when paired with NASA DONKI — Space Weather Intelligence through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your NASA DONKI — Space Weather Intelligence integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your NASA DONKI — Space Weather Intelligence connection logic from agent behavior for testable, maintainable code

NASA DONKI — Space Weather Intelligence + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the NASA DONKI — Space Weather Intelligence MCP Server delivers measurable value.

Type-safe data pipelines: query NASA DONKI — Space Weather Intelligence with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple NASA DONKI — Space Weather Intelligence tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query NASA DONKI — Space Weather Intelligence and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock NASA DONKI — Space Weather Intelligence responses and write comprehensive agent tests

NASA DONKI — Space Weather Intelligence MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect NASA DONKI — Space Weather Intelligence to Pydantic AI via MCP:

get_cme

CMEs are massive bursts of solar wind and magnetic fields from the Sun. Earth-directed CMEs cause geomagnetic storms and aurora. Default: last 30 days. Get Coronal Mass Ejection (CME) events from NASA DONKI

get_donki_notifications

A unified feed of all space weather events: CMEs, flares, storms, shocks, and radiation events. Good for a quick overview of recent solar activity. Get all recent DONKI space weather notifications

get_geomagnetic_storms

Includes Kp index and linked CME/shock data. Storms above Kp=7 are severe, Kp=9 is extreme. Affects power grids, GPS, satellites, and enables aurora at low latitudes. Get geomagnetic storm events from NASA DONKI

get_interplanetary_shocks

Shocks often precede geomagnetic storms and are caused by CME-driven disturbances in the solar wind. Get interplanetary shock wave events from NASA DONKI

get_radiation_belt

The Van Allen radiation belts can be energized during geomagnetic storms, posing risks to satellites in medium Earth orbit. Get radiation belt enhancement events from NASA DONKI

get_solar_energetic_particles

SEPs are dangerous to astronauts and can damage satellite electronics. Get Solar Energetic Particle (SEP) events from NASA DONKI

get_solar_flares

Classes: C (common), M (moderate), X (extreme). X-class flares cause radio blackouts and satellite disruption. Includes begin/peak/end times, active region, and instruments that detected it. Get solar flare events by class (C, M, X) from NASA DONKI

Example Prompts for NASA DONKI — Space Weather Intelligence in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with NASA DONKI — Space Weather Intelligence immediately.

"Were there any solar flares this month?"

"Were there any major geomagnetic storms last year?"

"Check if any interplanetary shocks are approaching."

Troubleshooting NASA DONKI — Space Weather Intelligence MCP Server with Pydantic AI

Common issues when connecting NASA DONKI — Space Weather Intelligence to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

NASA DONKI — Space Weather Intelligence + Pydantic AI FAQ

Common questions about integrating NASA DONKI — Space Weather Intelligence MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your NASA DONKI — Space Weather Intelligence MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.