Can my AI track a specific flight in real-time and show me its current position, altitude, and speed?

Yes! Use the `search_flights` tool with the flight number (e.g., "UAL123") to find the active flight, then use `track_flight` with the flight ID to get real-time position coordinates (latitude, longitude), altitude in feet, ground speed in knots, heading direction, and vertical speed. Your AI agent will respond with the exact location of the aircraft and its current flight parameters. Perfect for passenger pickup coordination and live flight monitoring.

How do I find all flights departing from or arriving at a specific airport?

Simply ask the agent to run the `get_airport_flights` action with the airport IATA or ICAO code (e.g., "JFK" or "KJFK" for New York JFK). It will return all arriving and departing flights with airline, flight number, aircraft type, origin/destination, scheduled and actual times, and current status. You can also use `search_airports` first to find the correct airport code if you only know the city name.

Can I look up information about a specific aircraft by its tail number or registration?

Absolutely! Use the `get_aircraft_info` tool with the aircraft registration/tail number (e.g., "N12345" for US, "G-EUUU" for UK, "D-AIMA" for Germany). Your AI will return the aircraft manufacturer and model, registration country, owner/operator, year built, engine type, number of engines, aircraft age, and category (airline, business jet, private, cargo). You can also use `search_aircraft` with an operator name to see their entire fleet.

Netrows MCP Server for CrewAI 12 tools — connect in under 2 minutes

Name: Netrows
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Netrows through Vinkius, pass the Edge URL in the `mcps` parameter and every Netrows tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Netrows Specialist",
    goal="Help users interact with Netrows effectively",
    backstory=(
        "You are an expert at leveraging Netrows tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Netrows "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Netrows MCP Server

Connect your Netrows Aviation API flight tracking platform to any AI agent and take full control of real-time flight monitoring, aircraft intelligence, airport operations, and airline schedule analysis through natural conversation.

When paired with CrewAI, Netrows becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Netrows tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Flight Search — Find active and recent flights by flight number, callsign, or origin-destination airport pair
Flight Details — Get comprehensive flight information including airports, times, aircraft, and status
Real-Time Tracking — Monitor live flight positions with coordinates, altitude, speed, and heading
Aircraft Registry — Look up aircraft specifications, ownership, registration, and fleet details
Fleet Analysis — Search all aircraft operated by specific airlines or aviation companies
Airport Intelligence — Query airport static data, codes, locations, and timezone information
Airport Activity — Monitor all arriving and departing flights at any airport worldwide
Airport Search — Find all airports serving a specific city or metropolitan area
Flight Schedules — Access complete flight schedules between any two airports
Airline Monitoring — Track all active flights by airline with real-time operational data
Airline Profiles — Get airline company information including fleet size, hubs, and destinations
Account Usage — Monitor your API credit consumption and remaining quota

The Netrows MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Netrows to CrewAI via MCP

Follow these steps to integrate the Netrows MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 12 tools from Netrows

Why Use CrewAI with the Netrows MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Netrows through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Netrows + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Netrows MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Netrows for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Netrows, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Netrows tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Netrows against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Netrows MCP Tools for CrewAI (12)

These 12 tools become available when you connect Netrows to CrewAI via MCP:

get_account_usage

The Netrows API operates on a credit-based system where each API call consumes 1 credit. Essential for monitoring API consumption, budget management, rate limit awareness, and planning integration usage patterns. AI agents should query this when users ask "how many credits do I have left", "what is my API usage this month", or need to monitor their API consumption before running large batch queries. Check your API account usage and remaining credits

get_aircraft_info

g., "N12345" for US-registered, "G-EUUU" for UK). Returns aircraft type (manufacturer and model), registration country, owner/operator information, registration status, year built, engine type (jet, turboprop, piston), number of engines, aircraft age, and category (airline, business jet, private, cargo). Critical for aviation enthusiasts, fleet tracking, aircraft utilization analysis, and private aviation monitoring. AI agents should reference this when users ask "tell me about aircraft N12345", "who owns this tail number", or need aircraft specifications to contextualize flight data. Get registration details and specifications for a specific aircraft

get_airline_flights

g., "UA" for United, "DL" for Delta, "BA" for British Airways). Returns flight numbers, aircraft registrations and types, origin-destination pairs, scheduled and actual times, and current status for all flights in the airline fleet. Essential for airline operations monitoring, fleet utilization analysis, competitor intelligence, and passenger rebooking during disruptions. AI agents use this when users ask "show me all United flights right now", "what is Delta flying", or need to track an entire airline operational picture in real-time. List all active flights operated by a specific airline

get_airline_info

Returns airline name, IATA/ICAO codes, callsign, country of registration, fleet size, destination count, hub airports, and operational status. Essential for airline industry research, competitor analysis, travel planning context, and aviation market intelligence. AI agents should reference this when users ask "tell me about United Airlines", "what is the ICAO code for Delta", or need airline metadata to contextualize flight and fleet data. Get information and details for a specific airline

get_airport_flights

Returns a comprehensive list of inbound and outbound flights with airline/operator, flight number, aircraft type, origin/destination airports, scheduled and actual times, and current flight status (en-route, landed, scheduled, delayed, cancelled, diverted). Essential for airport operations management, passenger pickup coordination, ground handling planning, and flight activity monitoring. AI agents should reference this when users ask "what flights are at X airport", "show me all activity at Y", or need to monitor airport traffic patterns. List all arriving and departing flights at a specific airport

get_airport_info

g., "JFK" or "KJFK" for New York JFK, "LAX" or "KLAX" for Los Angeles International). Returns airport name, location (city, state, country), IATA/ICAO/FAA codes, geographic coordinates (latitude, longitude, elevation), timezone, and operational status. Essential for airport identification, travel planning, flight briefing preparation, and geographic reference. AI agents should use this when users ask "tell me about airport X", "what is the ICAO code for Y", or need airport metadata to contextualize flight queries. Get static information and details for a specific airport

get_flight_details

Returns departure and arrival airports with full metadata (IATA/ICAO codes, terminal, gate), scheduled and actual times for departure and arrival, aircraft registration and type, airline/operator details, current flight status, and tracking coordinates if airborne. Critical for passenger travel updates, airline operations coordination, and flight tracking dashboards. AI agents should reference this when users request detailed status for a known flight, including gate assignments, timing comparisons, and aircraft information. Get complete details for a specific flight

get_flight_schedule

Returns all scheduled flights with airline/operator, flight numbers, aircraft types, departure and arrival times, frequency of service, and days of operation. Essential for route planning, travel itinerary preparation, schedule analysis, and aviation market research. AI agents should reference this when users ask "what flights fly from JFK to LAX", "show me the schedule between ORD and DFW", or need to plan travel between specific airport pairs with comprehensive scheduling options. Get scheduled flights between two airports

search_aircraft

Returns all registered aircraft in the operator fleet with registration numbers, aircraft types (manufacturer and model), ages, and current operational status. Essential for fleet analysis, aviation industry research, competitor intelligence, and operator profile generation. AI agents use this when users ask "show me all United Airlines aircraft", "what planes does Delta operate", or need to analyze fleet composition for a specific aviation operator. Search for all aircraft operated by a specific airline or company

search_airports

Returns all airports (major international, regional, and general aviation) associated with the queried city including IATA/ICAO codes, full names, locations, distances from city center, and airport types. Essential for travel planning, multi-airport city analysis, alternate airport identification, and geographic aviation research. AI agents use this when users ask "what airports serve Chicago", "find airports in London", or need to identify all airports in a metropolitan area for comprehensive flight searches. Search for airports by city name or location

search_flights

The query can be a flight number (e.g., "UAL123"), callsign, or origin-destination airport pair. Returns complete flight identification, airline/operator, aircraft type, departure and arrival airports with IATA/ICAO codes, scheduled and actual times, current position coordinates (latitude, longitude), altitude in feet, ground speed in knots, heading, and flight status (en-route, landed, diverted, cancelled). Essential for real-time flight tracking, passenger pickup coordination, logistics planning, and aviation operations monitoring. AI agents should use this when users ask "where is flight X", "what flights are flying from A to B", or need to track specific flights by number or route. Search for active and recent flights by flight number, callsign, or route

track_flight

Returns timestamped position data that can be used to visualize flight progress on maps, estimate arrival times, and monitor flight trajectory. Essential for live flight tracking applications, passenger monitoring, operations dashboards, and aviation enthusiast displays. AI agents use this when users ask "track this flight live", "where is this aircraft right now", or need continuous position updates for an airborne flight. Track real-time position and status of a specific flight

Example Prompts for Netrows in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Netrows immediately.

"Search for all active United Airlines flights from Newark to San Francisco."

"Show me all airports that serve the city of London and their current flight activity."

"Tell me about aircraft N12345 — who owns it, what type is it, and what flights has it been operating?"

Troubleshooting Netrows MCP Server with CrewAI

Common issues when connecting Netrows to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Netrows + CrewAI FAQ

Common questions about integrating Netrows MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.