How to Use the NASA Open Data MCP in Pydantic AI

Q: How do I add the NASA Open Data MCP Server to Pydantic AI?

Install "pydantic-ai-slimmcp" via pip. Create an MCPToolset pointing to your Vinkius URL, and pass it to the toolsets argument when building your agent.

Enforce strict type safety on NASA telemetry with Pydantic AI.

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Connect NASA Open Data MCP to Pydantic AI

Create your Vinkius account to connect NASA Open Data to Pydantic AI 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 Open Data with Pydantic AI

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Type-Safe NEO Feeds in Pydantic AI

The `get_near_earth_objects_feed` tool pipes asteroid tracking data into Pydantic AI for rigorous runtime validation. If the API returns a string instead of a float for the miss distance, your agent fails loudly. No silent corruption creeps into your space weather application. You connect the asteroid tracker using the unified `MCPToolset` class. The framework automatically maps the JSON schema from the endpoint to your internal Python models before the agent ever sees the miss distance payload.

Verify Space Weather Events

The `get_solar_flares` and `get_coronal_mass_ejections` tools deliver structured space weather events directly to your model-agnostic agent. You define the exact fields you expect from the API. The agent queries the database, and the framework verifies the peak flux timestamps against your strict datetime constraints. This setup executes exactly the same way on local weights as it does on a hosted API provider. The toolset handles the Streamable HTTP transport, allowing your application to focus purely on validating coronal mass ejections.

Enforce Mars Manifest Parameters

The `get_mars_rover_photos` tool fetches raw imagery based on the exact parameters specified by the `get_mars_rover_manifest` endpoint. Your agent constructs the request, and the type-checker guarantees the camera names match the allowed literal values. Bad requests never even hit the network. We dropped the deprecated MCPServerHTTP method for this integration. You just pass the configured toolset array to your agent, and it gains immediate, type-safe access to the entire Martian image catalog.

Setup guide

Set up NASA Open Data MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "nasa-open-data-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to NASA Open Data tools.",
)

result = await agent.run("List recent NASA Open Data transactions")
print(result.output)

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Common questions about NASA Open Data MCP in Pydantic AI

Install `"pydantic-ai-slim[mcp]"` via pip. Create an `MCPToolset` pointing to your Vinkius URL, and pass it to the `toolsets` argument when building your agent.

Yes. The framework parses the incoming MCP tool schemas and enforces strict type checking on every response. If an asteroid diameter comes back in the wrong format, the execution throws a validation error immediately.

The unified toolset supports both Streamable HTTP and SSE transports. You just provide the correct URL scheme, and the framework handles the underlying connection.

Yes. The framework is completely model-agnostic. You can route the solar flare data to a local weights instance or a hosted API provider without changing the tool configuration.

The tool strictly handles peak flux timestamps and active region numbers from the public database. It executes outside your core application memory and never transmits your internal Pydantic models to the external API.

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