Can my AI check when the next bus is arriving at a specific UK bus stop?

Yes! First use `search_stops` to find the bus stop by name or location (e.g., "Oxford Street" or "Piccadilly Circus"). This returns the Naptan stop ID. Then use `get_bus_departures` with that stop ID to see all upcoming departures with expected arrival times, line numbers, destinations, and any delay information. You can also use `get_bus_arrivals` to monitor incoming services. The API provides real-time predictions based on live vehicle tracking across Great Britain.

How do I plan a complete journey from my hotel to a UK attraction using public transport?

Use the `get_journey_plan` tool with your hotel address or postcode as the origin and the attraction name or postcode as the destination. The TransportAPI journey planner will return complete multimodal itineraries combining buses, trains, trams, underground (tube), walking, and cycling with departure times, arrival times, total duration, number of changes, detailed legs with line names and operators, walking distances, and real-time disruption alerts. You can also specify a desired departure or

Can I check if there are any disruptions or engineering works affecting UK rail services?

Absolutely! Use `get_updates` to fetch current service alerts and disruption notices across the UK transport network. You can filter by mode ("rail" for trains, "bus" for buses) to get targeted alerts. The results include affected lines or stations, disruption descriptions, severity levels, expected duration, and alternative route recommendations. For station-specific information, use `get_rail_departures` or `get_rail_arrivals` which include cancellation and delay indicators for individual serv

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

Name: TransportAPI
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to TransportAPI through Vinkius, pass the Edge URL in the `mcps` parameter and every TransportAPI 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

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="TransportAPI Specialist",
    goal="Help users interact with TransportAPI effectively",
    backstory=(
        "You are an expert at leveraging TransportAPI 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 TransportAPI "
        "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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* 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 TransportAPI MCP Server

Connect your TransportAPI UK public transport data platform to any AI agent and take full control of real-time bus and rail tracking, multimodal journey planning, and service disruption monitoring across Great Britain through natural conversation.

When paired with CrewAI, TransportAPI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call TransportAPI 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

Real-Time Bus Tracking — Check upcoming departures and arrivals at any UK bus stop with ETAs and delay indicators
Rail Services — Monitor train arrivals, departures, and services at any UK rail station
Journey Planning — Plan door-to-door multimodal trips combining bus, rail, tram, underground, walking, and cycling
Stop Discovery — Search UK bus stops by name, address, or landmark with Naptan identifiers
Route Analysis — Get train route information between any two UK rail stations with calling points
Service Updates — Check real-time disruption alerts and operational notices across UK transport networks
Bus Timetables — Access complete timetables for any UK bus line with weekday/weekend patterns
Station Information — Get detailed UK rail station data including facilities, accessibility, and managing TOCs
Stop Details — Retrieve comprehensive bus stop information with served lines and accessibility features

The TransportAPI 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 TransportAPI to CrewAI via MCP

Follow these steps to integrate the TransportAPI 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 TransportAPI

Why Use CrewAI with the TransportAPI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with TransportAPI 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

TransportAPI + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries TransportAPI 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 TransportAPI, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain TransportAPI 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 TransportAPI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

TransportAPI MCP Tools for CrewAI (12)

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

get_bus_arrivals

Returns list of arriving services with line names and numbers, origins, scheduled and real-time arrival times (ETA), expected wait times, direction, operator details, and any delay indicators. Essential for passenger pickup coordination, arrival monitoring, and real-time arrival boards. AI agents use this when users ask "when is the next bus arriving at this stop", "show incoming buses at stop X", or need to track arriving bus services for passenger coordination. Get real-time bus arrivals at a specific UK stop

get_bus_departures

Returns list of departing services with line names and numbers, destinations, scheduled and real-time departure times (ETD), expected wait times, direction, operator details, and any service disruption notices. Covers all bus services across Great Britain including London Buses, Transport for Greater Manchester, West Midlands, and regional operators. Essential for passenger information displays, departure boards, journey planning, and real-time transit monitoring. AI agents should reference this when users ask "when is the next bus from this stop", "show departures from stop ID X", or need to monitor upcoming bus services at a known UK bus stop. Get real-time bus departures from a specific stop in the UK

get_journey_plan

Supports multimodal trips combining bus, rail, tram, underground (tube), walking, and cycling. Returns complete itinerary with departure and arrival times, total duration, number of changes, legs with mode details (line name, operator, vehicle type), intermediate stops/stations, walking distances, and real-time disruption information. Essential for travel planning, multimodal journey optimization, passenger information systems, and UK-wide mobility applications. AI agents should use this when users ask "how do I get from London Victoria to Heathrow Airport", "plan a journey from Manchester Piccadilly to Old Trafford", or need door-to-door trip planning across UK public transport. Plan a multimodal journey between two UK locations

get_rail_arrivals

Returns list of arriving services with train operating companies, origins, scheduled and real-time arrival times (ETA), platforms, expected delays, cancellation status, and service type information. Covers all National Rail services. Essential for passenger pickup coordination, arrival monitoring, station management, and real-time arrival boards. AI agents use this when users ask "what trains are arriving at Kings Cross", "show incoming trains at Manchester Piccadilly", or need to track arriving rail services. Get real-time train arrivals at a specific UK rail station

get_rail_departures

Returns list of departing services with train operating companies, destinations, scheduled and real-time departure times (ETD), platforms, expected delays, cancellation status, calling points, and service type (express, local, sleeper). Covers all National Rail services across Great Britain. Essential for departure boards, journey planning, station operations, and passenger information. AI agents should use this when users ask "what trains are leaving Paddington", "show departures from Birmingham New Street", or need comprehensive departure listings for a UK rail station. Get real-time train departures from a specific UK rail station

get_rail_route

Returns available services, journey duration, number of changes, calling points, train operating companies, typical frequency, and first/last service times. Essential for rail journey planning, route comparison, travel itinerary preparation, and understanding rail connectivity. AI agents should reference this when users ask "what is the train route from London to Manchester", "show rail connections between Edinburgh and Glasgow", or need to understand rail service options between two UK stations. Get train route information between two UK rail stations

get_rail_services

Returns services with train operating companies (TOCs), destinations, origins, scheduled times, platforms, service types (express, local, sleeper), and any disruption information. Covers National Rail services across Great Britain. Essential for station information displays, service monitoring, rail journey planning, and operational awareness. AI agents should reference this when users ask "what services call at Euston", "show all trains at Edinburgh Waverley", or need comprehensive service listings for a UK rail station. Get all train services calling at a specific UK rail station

get_station_info

Returns station name, location (address, latitude, longitude), facilities (ticket office, ticket machines, waiting room, car park, cycle storage, WiFi, step-free access), staffing hours, managing train operating company, annual entry/exit statistics, and accessibility information. Essential for station planning, accessibility assessment, facility verification, and passenger information. AI agents should use this when users ask "tell me about Clapham Junction station", "does Euston have step-free access", or need detailed station metadata for UK rail journey planning. Get detailed information about a specific UK rail station

get_stop_info

Returns stop name, location (latitude, longitude, address, locality, landmark), common services, served lines, stop type (bus stop, bus station, coach station), accessibility features (wheelchair access, sheltered, seating), and operator information. Essential for stop identification, accessibility planning, transit network analysis, and passenger information. AI agents should use this when users ask "tell me about this bus stop", "what lines serve stop X", or need detailed stop metadata to contextualize transit queries. Get detailed information about a specific UK bus stop

get_timetable

Returns all scheduled services with departure times from origin through to terminus, stops served in sequence, journey duration variations by time of day, weekday/weekend/holiday service patterns, operator information, and any planned service changes. Essential for comprehensive schedule analysis, journey planning at specific times, service pattern research, and understanding bus frequency throughout the day. AI agents use this when users ask "show me the full timetable for bus route 73", "what times does the X59 run on Sundays", or need complete schedule data for a UK bus service. Get full timetable for a specific UK bus line

get_updates

Returns active alerts with affected lines, services, or operators, disruption descriptions, severity levels, expected duration, alternative route recommendations, and timestamps. Covers bus, rail, tram, and underground services across Great Britain. Essential for disruption awareness, passenger communication, journey reliability monitoring, and travel planning during service changes. AI agents should reference this when users ask "are there any disruptions on the Northern Line", "is there engineering work on Great Western Railway", or need to check service reliability before planning UK journeys. Get real-time service updates and disruption alerts for UK transport

search_stops

Returns matching stops with Naptan stop IDs, names, locations (latitude, longitude), served lines, localities, and stop types. Essential for stop discovery, journey planning interfaces, transit stop identification, and building location-based transit features. AI agents should use this when users ask "find the bus stop near Oxford Street", "search for stops called Piccadilly", or need to identify Naptan stop IDs for use in departure/arrival queries. Search for UK bus stops by name, location, or landmark

Example Prompts for TransportAPI in CrewAI

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

"Show me all bus departures from Oxford Circus in the next 30 minutes."

"What trains are departing from London Paddington to Bristol in the next 2 hours?"

"Plan a journey from Manchester Airport to the city centre using public transport."

Troubleshooting TransportAPI MCP Server with CrewAI

Common issues when connecting TransportAPI 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.

TransportAPI + CrewAI FAQ

Common questions about integrating TransportAPI 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.