How to Use the AirLabs MCP in OpenAI Agents SDK

Q: How do I connect AirLabs to the OpenAI Agents SDK?

Install the openai-agents package first. Create an MCPServerStreamableHttp instance with your AirLabs endpoint URL. Pass it to your Agent constructor using the mcpservers list.

Q: Can the AirLabs MCP Server handle partial airport names in OpenAI Agents SDK?

Yes, the autocompleteairport tool handles partial matches. It returns ranked suggestions with IATA codes and full city names. Your agent can use this to resolve ambiguous user input before making schedule queries.

Q: Why isn't my OpenAI agent finding nearby airports with AirLabs?

The getnearbyairports tool requires precise geographic coordinates and a search radius. Check your OpenAI tracing dashboard to see if the agent passed valid latitude and longitude values. Turn on cacheToolsList=True to ensure the tool definition loaded correctly.

Build production-ready aviation tracking systems with the AirLabs MCP Server and OpenAI Agents SDK.

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Connect AirLabs MCP to OpenAI Agents SDK

Create your Vinkius account to connect AirLabs 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 AirLabs with OpenAI Agents SDK

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Real-Time Flight Tracking with OpenAI Agents SDK

`get_flights` pulls active worldwide flight data directly into your agent context. You pass a bounding box or an airline code, and the tool returns altitude, ground speed, and exact coordinates. `get_flight_info` handles specific queries like "UA123", fetching terminal assignments and baggage claim numbers instantly. Your OpenAI agent uses these tools to monitor live operations. Built-in guardrails validate the bounding box coordinates before execution. Tracing logs every API call, so you know exactly when your agent checked the flight status.

Airport Operations and Delay Monitoring

`get_airport_delays` retrieves current delay averages, cancellation rates, and weather indicators for any specific airport. Combine this with `get_flights_by_airport` to pull full arrival and departure boards. The agent sees exactly what the ground handlers see. Handoffs work perfectly here. One specialized agent monitors delays and passes the context to a notification agent. You build a system that detects a spike in JFK departure delays and automatically checks alternative `get_schedules` for affected passengers.

Fleet Intelligence and Routing via MCP Server

`get_airline_fleet` exposes every aircraft an airline operates, including age, engine type, and storage status. `get_airline_routes` maps the entire network of origin-destination pairs. You get the raw data needed for competitive analysis. The MCP standard makes this simple to integrate. Your agent queries `get_aircraft` using a transponder hex code to identify a specific plane. It then checks `get_airlines` to pull the operator's corporate profile, all without writing custom API wrappers.

Setup guide

Set up AirLabs 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 AirLabs tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate AirLabs 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="AirLabs Agent",
            instructions="You have access to AirLabs 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 AirLabs MCP in OpenAI Agents SDK

Install the `openai-agents` package first. Create an `MCPServerStreamableHttp` instance with your AirLabs endpoint URL. Pass it to your Agent constructor using the `mcp_servers` list.

Yes, the `autocomplete_airport` tool handles partial matches. It returns ranked suggestions with IATA codes and full city names. Your agent can use this to resolve ambiguous user input before making schedule queries.

The `get_nearby_airports` tool requires precise geographic coordinates and a search radius. Check your OpenAI tracing dashboard to see if the agent passed valid latitude and longitude values. Turn on `cacheToolsList=True` to ensure the tool definition loaded correctly.

The server tracks both. You can look up private planes using `get_aircraft` if you have the hex code. The `get_airports` tool also returns small regional and general aviation facilities.

This server only reads public aviation data like flight schedules, aircraft hex codes, and airport delays. It never touches passenger manifests or personal identifiable information. Vinkius runs the connection inside a V8 Isolate sandbox, so your agent's queries disappear the moment the session ends.

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