How to Use the NOAA Forecast — US Weather Predictions MCP in Pydantic AI

Q: How do I add these NOAA tools to my Pydantic AI agent?

Install the package with pip install "pydantic-ai-slimmcp". Then create an MCPToolset instance with your Vinkius server URL and pass it to your agent. The tools are ready to use.

Q: What happens if the NWS API returns data in a weird format?

Pydantic AI will raise a ValidationError. Your agent will stop instead of processing bad data. This is the main reason to use it—it protects your application from unexpected API changes.

Build weather tools that don't break. Get type-safe, validated NOAA forecast data in your Pydantic AI agent.

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Connect NOAA Forecast — US Weather Predictions MCP to Pydantic AI

Create your Vinkius account to connect NOAA Forecast — US Weather Predictions 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 NOAA Forecast — US Weather Predictions with Pydantic AI

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Get Forecasts That Match Your Models

When your agent calls `get_forecast`, it doesn't just get a blob of JSON. Pydantic AI automatically parses the 7-day forecast from the NWS API into a Pydantic model you define. Every field—temperature, wind speed, precipitation—is checked. If the NWS ever changes its API schema or returns an unexpected value, your code doesn't silently fail or corrupt data. It raises a `ValidationError` immediately. You know exactly what went wrong and where, making your application far more reliable.

Chain Calls with Guaranteed Correctness

Build multi-step weather queries you can trust. First, call `get_point_metadata` with a latitude and longitude. Pydantic AI validates the response, guaranteeing you have the correct `wfo` and `gridX`/`gridY` coordinates. Then, feed those validated coordinates into `get_hourly_forecast`. Because the input to the second step was verified by the first, you eliminate a whole class of errors. Your agent works with clean, structured data at every stage.

Use Any LLM with this Pydantic AI MCP Server

Pydantic AI is model-agnostic. You can use this NOAA MCP server with an agent powered by OpenAI, Anthropic, Gemini, or even a local model running on your machine. The tool-calling logic remains the same. The validation happens in your Python code, not in the model. This means you get the same reliable, type-safe NWS weather data regardless of which LLM is driving the agent. You can switch models without rewriting your data-handling code.

Setup guide

Set up NOAA Forecast — US Weather Predictions 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": {
        "noaa-forecast-us-weather-predictions-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to NOAA Forecast — US Weather Predictions tools.",
)

result = await agent.run("List recent NOAA Forecast — US Weather Predictions transactions")
print(result.output)

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Common questions about NOAA Forecast — US Weather Predictions MCP in Pydantic AI

Install the package with `pip install "pydantic-ai-slim[mcp]"`. Then create an `MCPToolset` instance with your Vinkius server URL and pass it to your agent. The tools are ready to use.

Pydantic AI will raise a `ValidationError`. Your agent will stop instead of processing bad data. This is the main reason to use it—it protects your application from unexpected API changes.

It provides the data, and Pydantic AI provides the guarantee. By using the `get_hourly_forecast` tool, you get 156 hours of data that is instantly validated against your Pydantic models, ensuring your app's logic is built on a solid foundation.

Absolutely. The tool will return the raw text from the meteorologist's discussion. Pydantic AI will ensure the response is a valid string, so your agent can then pass it to an LLM for summarization or analysis.

Your Pydantic AI agent sends the coordinates to the Vinkius server. The server uses them in a temporary, isolated environment to fetch data from NOAA and then discards them. Pydantic's validation also adds a layer of security by ensuring no malformed location data can be processed by your app.

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