MCP Servers & Agent Skills for Aviation Data

How do you connect aviation data to an LLM with MCP?

An MCP server sits between the model and your data. It publishes a set of tools — for example, look up a flight, list departures from an airport, or fetch active NOTAMs for a route — each with a typed input schema and a description the model can read. When a user asks a question, the assistant decides which tools to call, the server executes those calls against real systems, and the model answers from the results.

Because the model reads live data rather than relying on what it learned in training, answers about today's flights are current and checkable.

What can an aviation MCP server expose?

Common tool sets we build:

• Flight status, live position, and history by flight number, registration, or route
• Schedules and direct routes between airports, with frequency and equipment
• Delay predictions with the factors behind them
• Decoded NOTAMs filtered by airport, route, or time window
• Airport, airline, and aircraft reference lookups across IATA and ICAO codes
• Read and write access to internal systems — bookings, maintenance logs, rosters — under the permissions you define

What are agent skills, and how do they differ from tools?

A tool is a single capability. A skill is packaged know-how: instructions, examples, and tool combinations that teach an assistant how to complete a domain task correctly — for instance, how to assess whether a connection is at risk, or how to read a NOTAM for runway impact. Skills encode your procedures so that every user of the assistant gets the same, correct method.

How do you keep MCP integrations secure?

Each server authenticates the calling user and enforces that user's permissions, so the assistant can never see more than the person asking. Write operations are separated from reads and can require confirmation. Every tool call is logged. Servers can run inside your network so that sensitive operational data never leaves your environment.

AeroCraft's own aviation APIs are designed to be exposed this way, which makes them a fast starting point for an MCP deployment.

What does a first MCP deployment look like?

A typical first deployment exposes a small, read-only tool set — flight status, schedules between two airports, active NOTAMs for an airport, and reference lookups — to one team's AI assistant. Read-only tools carry little risk and immediately remove a large volume of manual look-ups from the working day.

Once the team trusts the answers, the server is extended with internal data sources and, where it makes sense, carefully permissioned write actions. Usage logs show which tools are called most and which questions the assistant could not answer, which tells us exactly what to build next instead of guessing.

Because MCP is an open standard, the work is not tied to one AI vendor. The same server can serve a desktop assistant for analysts today and an autonomous agent next year.