How to Use the AirLabs MCP in Pydantic AI

Q: How do I add the AirLabs MCP to my Pydantic AI setup?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your HTTP endpoint. Pass it to your Agent via the toolsets parameter.

Q: Does AirLabs support finding alternate airports in Pydantic AI?

The getnearbyairports tool handles this by taking geographic coordinates and a radius. It returns a structured list of facilities with exact distances. Your agent can use this to suggest diversions during weather events.

Q: How does Pydantic AI handle AirLabs delay data?

The agent calls getairportdelays to fetch current cancellation rates and delay averages. Pydantic validates the returned metrics against your schema. If the weather indicators are missing, the framework catches the validation error immediately.

Q: Can I monitor specific airport traffic with this MCP Server?

You use the getflightsbyairport tool to pull complete arrival or departure boards. It includes scheduled times, actual times, and baggage claim details.

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Connect AirLabs MCP to Pydantic AI

Create your Vinkius account to connect AirLabs 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 AirLabs with Pydantic AI

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Type-Safe Flight Tracking with Pydantic AI

`get_flights` fetches live aircraft positions, altitudes, and squawk codes based on your search parameters. `get_flight_info` targets specific flight numbers to pull terminal and gate assignments. Every response maps directly to your expected data structures. Pydantic AI validates these responses at runtime. If the API returns a string where an integer altitude should be, the agent fails loudly. You never have to worry about silent data corruption or hallucinated flight coordinates entering your production database.

Strict Airport and Schedule Validation

`get_airports` retrieves geographic coordinates, timezones, and operational status for global facilities. `get_schedules` pulls exact timetables, frequencies, and assigned aircraft registrations. `autocomplete_airport` resolves fuzzy user inputs into strict IATA codes. Your agent relies on this exactness. It uses the validated IATA codes to query schedules without guessing. Because the framework is model-agnostic, you can swap between Anthropic or local models while maintaining the exact same validation guarantees.

Fleet Data via the AirLabs MCP Server

`get_airline_fleet` returns an airline's complete aircraft inventory, including delivery dates and operational status. `get_aircraft` looks up individual airframes using their unique hex codes. `get_airline_routes` exposes the entire network of origin-destination pairs. The unified protocol makes connecting these tools simple. You get guaranteed schemas for complex aviation data. Your agent parses fleet ages and engine types with absolute confidence that the fields exist and match your models.

Setup guide

Set up AirLabs 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": {
        "airlabs-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent AirLabs transactions")
print(result.output)

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Common questions about AirLabs MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` pointing to your HTTP endpoint. Pass it to your Agent via the `toolsets` parameter.

The `get_nearby_airports` tool handles this by taking geographic coordinates and a radius. It returns a structured list of facilities with exact distances. Your agent can use this to suggest diversions during weather events.

The agent calls `get_airport_delays` to fetch current cancellation rates and delay averages. Pydantic validates the returned metrics against your schema. If the weather indicators are missing, the framework catches the validation error immediately.

You use the `get_flights_by_airport` tool to pull complete arrival or departure boards. It includes scheduled times, actual times, and baggage claim details.

This server interacts exclusively with global aviation telemetry, such as flight paths, squawk codes, and terminal assignments. It never processes personal user data. Vinkius manages the connection via an ephemeral V8 Isolate, ensuring no request state persists after execution.

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