How to Use the NOAA Aviation — Airport Weather Intelligence MCP in OpenAI Agents SDK

Q: How do I connect the OpenAI Agents SDK to the NOAA Aviation — Airport Weather Intelligence MCP server?

You instantiate the MCPServerStreamableHttp class with your Vinkius endpoint URL. Then you pass that server object into the mcpservers list when you create your agent. The tools are discovered automatically.

Q: Can I get weather for multiple airports in one call?

Yes. The getmetar and gettaf tools both accept a comma-separated string of ICAO codes. This lets your agent get data for KJFK, EGLL, and LFPG all at once.

Q: What's a good pattern for handling new SIGMETs from NOAA Aviation — Airport Weather Intelligence in the OpenAI Agents SDK?

Set up a dedicated agent to poll the getsigmet tool on a timer. When the tool returns a new alert that matches your criteria (e.g., severe turbulence on a specific route), it can trigger a handoff to another agent to manage the response.

Q: Is it possible to get historical weather data with NOAA Aviation — Airport Weather Intelligence and the OpenAI Agents SDK?

You can get recent METARs. The getmetar tool has an optional parameter to retrieve observations from the past few hours. It's not designed for deep historical analysis, but it's perfect for seeing how conditions have changed recently.

Build production-grade aviation agents with the OpenAI Agents SDK that react to live NOAA weather data.

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Connect NOAA Aviation — Airport Weather Intelligence MCP to OpenAI Agents SDK

Create your Vinkius account to connect NOAA Aviation — Airport Weather Intelligence to OpenAI Agents SDK 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 Aviation — Airport Weather Intelligence with OpenAI Agents SDK

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Auto-Discover Tools with Guardrails

Your agent instantly discovers all five NOAA aviation tools. You don't configure anything. It just knows how to `get_metar` for JFK or `get_taf` for Heathrow. Before your agent even calls the API, you can use OpenAI's built-in guardrails to validate inputs. This means you can check for valid ICAO codes or sensible date ranges, stopping bad calls before they consume resources. It's a simple way to make your agent more robust.

Orchestrate Specialized Weather Agents

Don't build one giant agent. Build a team. Have one specialist agent that does nothing but monitor for new hazardous weather by calling `get_sigmet` every five minutes. When it finds a new icing or turbulence SIGMET, it hands off the specific report to a separate routing agent. That second agent then pulls fresh `get_taf` and `get_pirep` data for affected airports to plan a safe and efficient diversion. Everything is tracked and auditable in your OpenAI dashboard.

Build Auditable Production Systems

This isn't for toys. Use the OpenAI Agents SDK and this MCP Server to build a real flight operations tool. Your agent can monitor a fleet of flights, pulling pilot reports with `get_pirep` and current conditions with `get_metar`. Because every action and API call is traced, you have a complete audit log. When a flight is rerouted around a thunderstorm, you can see the exact `get_sigmet` data that triggered the decision. It's accountability for automated systems.

Setup guide

Set up NOAA Aviation — Airport Weather Intelligence MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all NOAA Aviation — Airport Weather Intelligence tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives NOAA Aviation — Airport Weather Intelligence tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate NOAA Aviation — Airport Weather Intelligence tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="NOAA Aviation — Airport Weather Intelligence Agent",
            instructions="You have access to NOAA Aviation — Airport Weather Intelligence tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about NOAA Aviation — Airport Weather Intelligence MCP in OpenAI Agents SDK

You instantiate the `MCPServerStreamableHttp` class with your Vinkius endpoint URL. Then you pass that server object into the `mcp_servers` list when you create your agent. The tools are discovered automatically.

Yes. The `get_metar` and `get_taf` tools both accept a comma-separated string of ICAO codes. This lets your agent get data for KJFK, EGLL, and LFPG all at once.

Set up a dedicated agent to poll the `get_sigmet` tool on a timer. When the tool returns a new alert that matches your criteria (e.g., severe turbulence on a specific route), it can trigger a handoff to another agent to manage the response.

You can get recent METARs. The `get_metar` tool has an optional parameter to retrieve observations from the past few hours. It's not designed for deep historical analysis, but it's perfect for seeing how conditions have changed recently.

None. The server only processes public aviation weather data like METARs, TAFs, and PIREPs. Vinkius runs each MCP Server in a zero-trust, ephemeral sandbox. Your connection is encrypted, and the server instance is destroyed after use, leaving no data behind.

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