How to Use the AeroAPI (FlightAware) MCP in Pydantic AI

Type-safe flight tracking for Pydantic AI using AeroAPI (FlightAware) tools.

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Connect AeroAPI (FlightAware) MCP to Pydantic AI

Create your Vinkius account to connect AeroAPI (FlightAware) 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 AeroAPI (FlightAware) with Pydantic AI

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Type-safe flight data with Pydantic AI

Every response from `get_flight_details` gets validated against your models. If the API returns garbage, the agent stops immediately. This keeps your flight data pipeline clean. You'll never deal with unexpected nulls or missing fields in your production code.

Airport queries for Pydantic AI

Call `list_airport_flights` to get a structured list of arrivals and departures. Your Pydantic models define the schema for these records. It prevents silent failures. If the airport data doesn't match your expected format, the agent raises a validation error for you to catch.

AeroAPI (FlightAware) tool verification

Run `check_api_status` as a pre-flight check in your agent workflow. It returns a boolean that your Pydantic AI logic can use to gate tasks. This adds a layer of safety before the agent attempts to search for a flight. You get immediate feedback if the server is offline.

Setup guide

Set up AeroAPI (FlightAware) 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": {
        "aeroapi-flightaware-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about AeroAPI (FlightAware) MCP in Pydantic AI

The framework automatically checks tool outputs against your Pydantic models. You define the schema, and the MCP server maps the data to it.

Yes, the server supports SSE transports. You just need to provide the correct endpoint URL to your MCPToolset.

The agent fails hard with a validation error. This prevents corrupted data from entering your database or triggering bad decisions.

Yes, the MCP tools are model-agnostic. You can swap between Anthropic, OpenAI, or local models without changing your tool definitions.

We enforce strict encryption for all communication between the client and our server. No flight data is cached or logged on our infrastructure.

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