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 OpenAI Agents SDK 12 tools — connect in under 2 minutes

Name: Lyko
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 12 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Lyko through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Lyko Assistant",
            instructions=(
                "You help users interact with Lyko. "
                "You have access to 12 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Lyko"
        )
        print(result.final_output)

asyncio.run(main())

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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 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.

The OpenAI Agents SDK auto-discovers all 12 tools from Lyko through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Lyko, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP

Follow these steps to integrate the Lyko MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

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

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 12 tools from Lyko

Why Use OpenAI Agents SDK with the Lyko MCP Server

OpenAI Agents SDK provides unique advantages when paired with Lyko through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Lyko + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Lyko MCP Server delivers measurable value.

Automated workflows: build agents that query Lyko, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Lyko, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Lyko tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Lyko to resolve tickets, look up records, and update statuses without human intervention

Lyko MCP Tools for OpenAI Agents SDK (12)

These 12 tools become available when you connect Lyko to OpenAI Agents SDK 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 OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK

Common issues when connecting Lyko to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Lyko + OpenAI Agents SDK FAQ

Common questions about integrating Lyko MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.