PRO Annual — 50 users, $106.80/year
Stop hopping between external flight trackers and your GitScrum board. When Claude runs the AeroDataBox MCP, you can pull real-time airport data or historical delay metrics directly into a task description. This keeps complex logistics updates contained right where they belong.
Claude
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task.delegate()
You create a high-priority ticket, delegating the research to Claude. You ask Claude to investigate the impact of known airport delays on Milestone 4’s timeline.
Claude identifies the need for current or historical flight data, calling specific tools like get_airport_delays and get_flight_history via your MCP-compatible client (like Cursor or VS Code).
Claude synthesizes the raw aviation data into a concise, actionable report attached to the ticket. You review the findings and update the project timeline directly on the board.
task.create()
Copy. Paste into a Claude-aware GitScrum task. Done.
I need to provide a status update on our client's onboarding, and I know their project depends on travel through JFK this week.
Using the AeroDataBox MCP, find the current operational delay statistics for John F. Kennedy International Airport (JFK) and summarize any major disruptions in bullet points. Then, draft a professional update note suitable for our client portal ticket.
We're planning resource allocation for next quarter, and I need to know how often the Chicago O'Hare (ORD) airport has caused delays historically.
I need you to analyze historical data for ORD over the last 90 days using the AeroDataBox MCP. Determine the average delay duration and identify the peak day of disruption so we can adjust our buffer time in GitScrum's sprint planning.
A team member is traveling to a new site today, and I need immediate confirmation on the flight status before they leave the office.
Check the real-time status of the nearest flight arriving at our target airport using the AeroDataBox MCP. Provide the final destination, estimated time of arrival, and any reported delays so I can update their travel ticket immediately in GitScrum.
Manually tracking project milestones that rely on physical travel is a nightmare. You spend hours checking airport websites and cross-referencing flight numbers across Slack threads, only to update the ticket hours later. This constant dashboard hopping kills focus.
The new flow changes everything. Claude reads the necessary real-time data via this MCP (AeroDataBox) and writes the outcome into GitScrum's task description. You get immediate, actionable intelligence—not just a raw data dump—so you can keep your project board accurate.
Open this workflow in GitScrum →Predicting delays is almost impossible without dedicated tools. Historically, PMs rely on gut feeling or slow-to-compile spreadsheets to estimate project buffer time based on airport reliability. This guesswork introduces massive risk into the backlog.
Claude solves this by running sophisticated historical analysis using AeroDataBox MCP. It provides quantifiable data points—like average delay periods for a route in winter versus summer—allowing you to build truly resilient, data-backed sprint plans.
claude.ask()
Claude, what was the global delay trend over the last fiscal quarter? Use AeroDataBox MCP to analyze historical data and summarize key findings for our executive review ticket.
I need a detailed breakdown of all flight alert subscriptions. Can Claude use the AeroDataBox MCP tools to list them so I can audit our current monitoring setup?
Can Claude figure out the estimated travel time between two specific airports, say LAX and SFO? Use the distance and time calculation tool in the AeroDataBox MCP.
Based on historical flight data for a given route, what is the typical punctuality rate? Have Claude use the appropriate AeroDataBox tools to generate this metric.
tools.list()
Each tool is one operation Claude runs on your behalf.
| tool_name | operation |
|---|---|
| get_global_delays | Claude can use this tool to fetch current global aviation delay metrics, helping you assess overall operational performance for a project. |
| get_alert_subscription | This function allows an AI agent to retrieve specific details about a flight alert subscription that was previously set up for tracking purposes. |
| convert_alert_subscription | Claude uses this tool when needed to migrate or update old, legacy flight alert subscriptions into the current credit-based monitoring system. |
| create_flight_alert | An AI agent can invoke this tool to create a new webhook subscription for specific flights, ensuring you are notified of delays automatically. |
| delete_alert_subscription | Claude uses this function when a monitored flight is complete or obsolete, allowing the user to remove the associated alert webhook cleanly. |
| get_airline_fleet | This allows an AI agent to retrieve a list of active aircraft models and types currently operated by a specific airline name. |
| get_airport_delays_historical | Claude can analyze this tool to gather historical airport delay statistics for a defined date, perfect for risk assessment in project planning. |
| get_airport_delays_period | This function lets an AI agent pull comprehensive historical data on airport delays across a specified date range for trend analysis. |
| get_flight_history | Claude can use this tool to retrieve detailed schedules and movement history for any given flight number or registration code. |
| get_global_delays_historical | This tool enables an AI agent to pull historical data on global airport delays, helping analyze seasonal or long-term operational trends. |
| get_nearest_flight | Claude can fetch the real-time status and details of the closest flight based on a provided flight number for immediate team updates. |
| list_alert_subscriptions | The AI agent uses this tool to display a full list of all active flight alert webhook subscriptions associated with your account. |
| refill_alert_balance | Claude utilizes this function when the user needs to top up their credit balance, ensuring continuous monitoring capability for critical flights. |
| get_airport_delays | This allows an AI agent to quickly pull current delay metrics for any airport right now, useful for urgent ticket updates. |
| get_airport_routes_stats | Claude can use this tool to get statistical data on how frequently certain routes are run from a specific airport, aiding resource planning. |
| get_airport_runways | This function provides detailed physical and operational information about the runways at an airport for highly technical analysis. |
| get_airports_by_ip | An AI agent uses this to find nearby airports based on the user's current IP address location, useful for initial logistics assessment. |
| get_alert_balance | Claude can check the remaining credit balance allocated for flight alerts so you know when monitoring coverage is running low. |
| get_distance_time | This tool calculates both the physical distance and the estimated time required to fly between two specified airport locations. |
| get_fids_absolute | The AI agent uses this to fetch all scheduled departures and arrivals for an airport within a precise, absolute time window (start/end date). |
| get_fids_relative | Claude can retrieve departure and arrival schedules using a relative time frame (e.g., 'today' or 'next 3 hours') from the airport. |
| get_flight_by_date | This allows an AI agent to check the status of a specific flight number on any given date, useful for planning future project milestones. |
| get_flight_delays | Claude runs this tool to pull historical punctuality statistics specifically for one flight number, giving deep insight into reliability. |
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