How often are observations updated?

Most NWS stations report hourly (METAR), with some reporting every 5-15 minutes during significant weather (SPECI).

What are METAR and SPECI reports?

METARs are routine hourly weather reports generated primarily at airports. SPECI reports are unscheduled updates issued when there is a significant change in weather conditions.

Can I check the status of a local weather radar?

Yes, you can query the operational status of NEXRAD sites across the country to see if a station is active, down for maintenance, or scanning.

NOAA Observations — US Current Conditions MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: NOAA Observations — US Current Conditions
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect NOAA Observations — US Current Conditions through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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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 NOAA Observations — US Current Conditions "
            "(5 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in NOAA Observations — US Current Conditions?"
    )
    print(result.data)

asyncio.run(main())

NOAA Observations — US Current Conditions

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About NOAA Observations — US Current Conditions MCP Server

Real-time sensor data from thousands of NWS stations.

Pydantic AI validates every NOAA Observations — US Current Conditions tool response against typed schemas, catching data inconsistencies at build time. Connect 5 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.

What you can do

Find Stations — Locate nearby weather stations by lat/lon
Current Conditions — Latest observation (temp, wind, pressure, humidity)
Recent History — Observation trend over past hours
Station Metadata — Details about each station
Radar Network — NEXRAD radar station status

The NOAA Observations — US Current Conditions MCP Server exposes 5 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 NOAA Observations — US Current Conditions to Pydantic AI via MCP

Follow these steps to integrate the NOAA Observations — US Current Conditions 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 5 tools from NOAA Observations — US Current Conditions with type-safe schemas

Why Use Pydantic AI with the NOAA Observations — US Current Conditions MCP Server

Pydantic AI provides unique advantages when paired with NOAA Observations — US Current Conditions 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 NOAA Observations — US Current Conditions integration code

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

Dependency injection system cleanly separates your NOAA Observations — US Current Conditions connection logic from agent behavior for testable, maintainable code

NOAA Observations — US Current Conditions + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the NOAA Observations — US Current Conditions MCP Server delivers measurable value.

Type-safe data pipelines: query NOAA Observations — US Current Conditions with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple NOAA Observations — US Current Conditions tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query NOAA Observations — US Current Conditions and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock NOAA Observations — US Current Conditions responses and write comprehensive agent tests

NOAA Observations — US Current Conditions MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect NOAA Observations — US Current Conditions to Pydantic AI via MCP:

get_latest_observation

Provide a 4-character station ID such as KJFK, KLAX, KORD, KDFW. Get current weather conditions from a specific NWS station

get_observation_history

Useful for seeing temperature trends, wind changes, and weather evolution over recent hours. Get recent observation history for a NWS station

get_radar_stations

List all NWS radar stations and their status

get_station_metadata

Useful for understanding where a station is and what data it provides. Get metadata about a specific NWS weather station

get_stations

Each station has a 4-character ID (e.g., KJFK, KLAX). US only. Use station IDs with get_latest_observation. Find nearby NWS weather observation stations by latitude/longitude

Example Prompts for NOAA Observations — US Current Conditions in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with NOAA Observations — US Current Conditions immediately.

"What's the current temperature at JFK Airport?"

"Find the closest weather stations to downtown Chicago."

"What's the weather trend for the past 6 hours in Denver?"

Troubleshooting NOAA Observations — US Current Conditions MCP Server with Pydantic AI

Common issues when connecting NOAA Observations — US Current Conditions to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

NOAA Observations — US Current Conditions + Pydantic AI FAQ

Common questions about integrating NOAA Observations — US Current Conditions 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 NOAA Observations — US Current Conditions MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.