Can my AI plan a complete multimodal trip from my hotel to a tourist attraction using public transit?

Yes! Use the `plan_trip` tool with your hotel address or name as the origin and the tourist attraction as the destination. The Lyko routing engine will return complete door-to-door itineraries combining buses, trains, subways, trams, and walking segments with departure times, arrival times, total duration, number of transfers, line names, operators, walking distances, and real-time delay information. You can also specify preferred transport modes or a desired departure time. Perfect for tourist

How do I check real-time departures and arrivals at a specific train station or bus stop?

First, use `search_stops` to find the stop by name (e.g., "Gare du Nord" or "Alexanderplatz"). Once you have the stop ID, use `get_departures` to see upcoming services with ETAs, platforms, and delay indicators, or `get_arrivals` to track incoming services. The results include line names, destinations, scheduled vs. real-time times, and operator information. This is perfect for passenger pickup coordination, connection planning, and monitoring service reliability.

Can I check if there are any service disruptions or strikes affecting transit operators in France or Germany?

Absolutely! Use `get_operators` with the country filter (e.g., "FR" for France, "DE" for Germany) to list all available operators, then use `get_network_status` with the operator ID (e.g., "sncf" for SNCF, "db" for Deutsche Bahn) to check current service disruptions, planned works, strike notifications, weather impacts, and line closures. This gives you real-time awareness of transit reliability across 300+ European operators. AI agents can proactively alert travelers to service issues before tr

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

Name: Lyko
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Lyko through Vinkius, pass the Edge URL in the `mcps` parameter and every Lyko 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="Lyko Specialist",
    goal="Help users interact with Lyko effectively",
    backstory=(
        "You are an expert at leveraging Lyko 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 Lyko "
        "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 Lyko MCP Server

Connect your Lyko Transit API mobility platform to any AI agent and take full control of European public transit planning, real-time departure monitoring, and multimodal journey optimization through natural conversation.

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

Trip Planning — Plan door-to-door intermodal journeys combining buses, trains, subways, trams, ferries, bike-sharing, and walking
Real-Time Departures — Check upcoming departures at any transit stop with ETAs, platforms, and delay indicators
Arrival Tracking — Monitor incoming services for passenger pickup and connection coordination
Stop Discovery — Search transit stops by name, address, or landmark across 300+ European operators
Nearby Stops — Find all transit stops near any geographic location with distance calculations
Stop Details — Get comprehensive stop information including served lines, accessibility, and amenities
Line Information — Research transit lines with operator details, service hours, and route characteristics
Line Routes — View complete stop sequences and route patterns for any transit line
Operator Directory — Browse 300+ transit operators across Europe with coverage areas and service modes
Network Status — Check service disruptions, planned works, strikes, and delay alerts for any operator
GTFS Feeds — Access raw GTFS transit data for offline analysis and academic research
Trip Booking — Book train tickets, bus passes, bike rentals, and other mobility services through Lyko Book

The Lyko 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 Lyko to CrewAI via MCP

Follow these steps to integrate the Lyko 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 Lyko

Why Use CrewAI with the Lyko MCP Server

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

Lyko + CrewAI Use Cases

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

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

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

Lyko MCP Tools for CrewAI (12)

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

book_trip

Supports booking train tickets, bus tickets, bike-sharing rentals, car-sharing reservations, and other mobility services available through the Lyko Book platform. Returns booking confirmation, payment details, ticket information, QR codes for validation, and cancellation policies. Availability and booking capabilities vary by operator and service type. Essential for Mobility-as-a-Service integration, ticket purchasing, service reservations, and end-to-end journey planning with booking. AI agents should use this when users ask "book this train ticket", "reserve a bike for this trip", or want to complete a mobility service reservation after planning a route. Book a transit trip or mobility service through Lyko Book

get_arrivals

Returns list of arriving services with line names and numbers, origins, scheduled and real-time arrival times (ETA), platform or bay information, delay indicators, and operator details. Essential for passenger pickup coordination, arrival monitoring, transit hub management, and real-time arrival boards. AI agents use this when users ask "when does the next train arrive at X", "show incoming services at this station", or need to track arriving services for passenger coordination. Get upcoming arrivals at a specific transit stop

get_departures

Returns list of departing services with line names and numbers, destinations, scheduled and real-time departure times (ETD), platform or bay information, delay indicators, and operator details. Supports buses, trains, trams, subways, and ferries across European transit networks. Essential for passenger information displays, departure boards, travel apps, and real-time transit monitoring. AI agents should reference this when users ask "when is the next bus from stop X", "show departures from this station", or need to monitor upcoming services at a known transit stop. Get next departures from a specific transit stop

get_line_info

Returns line name, number, type (bus, train, tram, subway, ferry), operator, color code, route description, service hours, frequency, and accessibility information. Essential for line identification, transit network exploration, service information queries, and route planning context. AI agents should reference this when users ask "tell me about line M1", "what operator runs bus line 42", or need line metadata to understand transit service characteristics. Get information about a specific transit line

get_line_routes

Returns route variants (e.g., direction A and B), complete stop sequences with order, scheduled frequencies, first and last service times, and any service variations (express vs. local, peak vs. off-peak). Essential for complete line visualization, stop sequence analysis, transit mapping, and understanding service patterns. AI agents use this when users ask "show me all stops on line X", "what is the full route of bus 42", or need to understand complete service patterns for a transit line. Get all routes and stops for a specific transit line

get_nearby_stops

Returns nearby stops with distances from the coordinate, stop names, locations, served lines, operators, and stop types, sorted by proximity. Essential for location-based transit discovery, passenger navigation, "stops near me" features, and geographic transit analysis. AI agents use this when users ask "what stops are near my current location", "find transit stops within 500m of these coordinates", or need to discover accessible transit options from a specific point. Find transit stops near a geographic location

get_network_status

Returns active service disruptions, planned works, line closures, delay information, weather impacts, strike notifications, and alternative service recommendations. Essential for real-time service monitoring, disruption awareness, passenger communication, and travel planning during service changes. AI agents should reference this when users ask "are there any disruptions on SNCF trains", "is the Berlin U-Bahn running normally", or need to check service reliability before planning trips. Get current network status and service alerts for a transit operator

get_operators

Returns operator names, IDs, countries, coverage areas, transport modes operated (bus, train, tram, subway, ferry), contact information, and service status. Covers 300+ operators across Europe including SNCF (France), DB (Germany), NS (Netherlands), RENFE (Spain), Trenitalia (Italy), and many regional and local operators. Essential for operator research, transit network scoping, country-specific transit analysis, and understanding service coverage. AI agents should use this when users ask "what transit operators are available in France", "list all train operators in Germany", or need to identify operators for a specific country or region. List public transit operators available in a country or region

get_stop_info

Returns stop name, location (latitude, longitude, address), served lines and routes, stop type (bus stop, train station, tram stop, subway station, ferry terminal), operator information, accessibility features (wheelchair access, elevators), and available amenities. Essential for stop identification, accessibility planning, transit network analysis, and passenger information. AI agents should use this when users ask "tell me about this stop", "what lines serve stop X", or need detailed stop metadata to contextualize transit queries. Get detailed information about a specific transit stop

get_transit_feed

Returns feed metadata, last update timestamp, included operators, coverage area, data freshness indicators, and download or access URLs. GTFS feeds contain static schedule data, route definitions, stop locations, fare information, and service calendars. Essential for transit data analysis, offline planning applications, academic research, and transit network visualization. AI agents use this when users need access to raw GTFS data, want to analyze transit schedules offline, or require complete network definitions for planning applications. Access GTFS transit feed data for a specific operator or region

plan_trip

Supports multiple transport modes including buses, trains, subways, trams, ferries, bike-sharing, car-sharing, and walking combinations. Returns complete itinerary with departure and arrival times, duration, number of transfers, legs with mode details (line name, operator, vehicle type), intermediate stops, walking distances, fares if available, and real-time delay information. Essential for travel planning, multimodal journey optimization, passenger information systems, and Mobility-as-a-Service (MaaS) applications. AI agents should use this when users ask "how do I get from X to Y by public transport", "plan a trip from Paris Gare du Nord to Versailles", or need intermodal route options with timing and transfer details. Plan an intermodal trip between two locations using public transit

search_stops

Returns matching stops with stop IDs, names, locations (latitude, longitude), served lines, operators, 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 Champs-Elysees", "search for stops called X", or need to identify stop IDs for use in departure/arrival queries. Search for transit stops by name or location

Example Prompts for Lyko in CrewAI

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

"Plan a trip from Paris Gare du Nord to the Palace of Versailles using public transit."

"Show me all departures from Berlin Alexanderplatz station in the next 30 minutes."

"What transit operators are available in the Netherlands, and is NS (Dutch Railways) running normally today?"

Troubleshooting Lyko MCP Server with CrewAI

Common issues when connecting Lyko 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.

Lyko + CrewAI FAQ

Common questions about integrating Lyko 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.