Can my AI plan a complete multimodal trip from a Paris metro station to a suburb using public transit?

Yes! Use the `plan_journey` tool with the origin station name or coordinates (e.g., "Gare du Nord, Paris" or "2.3553;48.8800") and the destination (e.g., "La Defense, Puteaux" or coordinates). Navitia will return complete multimodal itineraries combining metro, RER, bus, tram, and walking with departure times, arrival times, total duration, number of transfers, detailed legs with line names and operators, walking distances, real-time disruption alerts, and accessibility information. You can spec

How do I check if there are any metro or bus disruptions affecting my planned route in Paris?

Use the `get_disruptions` tool with the region parameter set to "fr-idf" for Ile-de-France (Paris region). This returns all active disruptions with affected lines, routes, stops, severity levels, cause types (incident, maintenance, strike, weather), start and end timestamps, and impact descriptions. You can also check disruptions directly within journey planning results, as Navitia automatically injects disruption information into journey responses. For station-specific checks, use `get_departur

Can I generate an isochrone map to see what areas I can reach within 30 minutes by public transit from my hotel?

Absolutely! Use the `get_isochrone` tool with your hotel coordinates as the origin and "1800" (30 minutes in seconds) as the max_duration parameter. Navitia will return a GeoJSON polygon showing all areas reachable within your time limit using public transit combinations. This is perfect for real estate location analysis, understanding neighborhood accessibility, job market research, and planning your accommodation based on transit connectivity. You can adjust the max_duration for different time

Navitia MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: Navitia
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to Navitia through the Vinkius — pass the Edge URL in the `mcps` parameter and every Navitia 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="Navitia Specialist",
    goal="Help users interact with Navitia effectively",
    backstory=(
        "You are an expert at leveraging Navitia 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 Navitia "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 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 Navitia MCP Server

Connect your Navitia multimodal transit API to any AI agent and take full control of European public transportation planning, real-time service monitoring, and accessibility analysis through natural conversation.

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

What you can do

Multimodal Journey Planning — Plan door-to-door trips combining metro, bus, tram, RER, regional rail, walking, cycling, bike-sharing, and car
Place Search — Find transit stops, stations, addresses, and POIs with autocomplete search across French and European networks
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 Schedules — Access complete timetables for any transit stop with weekday/weekend/holiday patterns
Nearby Discovery — Find all transit stops near any geographic coordinate with distance calculations
Service Disruptions — Check active alerts, strikes, maintenance works, and operational notices across networks
Line Exploration — Browse all transit lines by mode type (metro, bus, tram, rail) with operator affiliations
Network Analysis — Research transit operators including RATP, SNCF, TCL, RTM, and regional authorities
Isochrone Mapping — Generate accessibility maps showing reachable areas within time limits from any point
Coverage Discovery — List all available coverage regions with data validity periods and contributor information

The Navitia MCP Server exposes 11 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 Navitia to CrewAI via MCP

Follow these steps to integrate the Navitia 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 11 tools from Navitia

Why Use CrewAI with the Navitia MCP Server

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

Navitia + CrewAI Use Cases

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

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

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

Navitia MCP Tools for CrewAI (11)

These 11 tools become available when you connect Navitia to CrewAI via MCP:

get_arrivals

Returns list of arriving services with line names and codes, origins, scheduled and real-time arrival times, platform information, delay indicators, and mode types. Essential for passenger pickup coordination, arrival monitoring, connection planning, and real-time arrival boards. AI agents use this when users ask "when does the next train arrive at this station", "show incoming services at stop X", or need to track arriving services for passenger coordination. Supports both theoretical schedules and real-time arrival predictions when operator data feeds are available. Get upcoming arrivals at a specific transit stop

get_coverage

Shows which cities and metropolitan areas are covered, data freshness indicators, and the contributing transit authorities for each region. Essential for discovering which transit networks are accessible through the API, validating region IDs for subsequent queries, understanding data coverage scope, and planning integration scope. AI agents should use this when users ask "what cities does Navitia cover", "show me all available transit regions", or need to identify the correct region ID (e.g., "fr-idf" for Paris/Ile-de-France) before making region-specific queries for lines, disruptions, or journeys. List all available coverage regions in the Navitia platform

get_departures

Returns list of departing services with line names and codes, destinations, scheduled and real-time departure times, platform or bay information, delay indicators, direction codes, and physical/commercial mode types (metro, bus, tram, RER, Transilien). Supports real-time data when available from operators. Essential for passenger information displays, departure boards, real-time transit monitoring, and journey planning. AI agents should reference this when users ask "when is the next metro from this station", "show departures from stop ID X", or need to monitor upcoming services at a known transit stop. Use data_freshness parameter to choose base_schedule (theoretical timetable) or realtime (including disruptions and delays). Get upcoming departures from a specific transit stop

get_disruptions

Returns active disruptions with affected lines, routes, stops, and networks, disruption descriptions, severity levels (minor, major, blocking), start and end timestamps, cause types (incident, maintenance, strike, weather), impact descriptions, and detour or alternative service recommendations. Covers all modes including metro, bus, tram, RER, Transilien, and regional rail across French and European networks. 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 Paris metro", "is there a strike on SNCF trains", or need to check service reliability before planning journeys. Get active service disruptions and alerts for a transit region

get_isochrone

Returns GeoJSON polygon boundaries, reachable area statistics, travel time bands, and accessibility metrics. Essential for urban planning, real estate location analysis, accessibility studies, job market research, school catchment analysis, and understanding transit connectivity. AI agents use this when users ask "what area can I reach within 30 minutes by metro from this address", "show me the accessible zone in 45 minutes by public transport", or need to analyze geographic accessibility from a specific location for housing, employment, or service planning. Generate an isochrone map showing reachable area from a point within a time limit

get_lines

Returns lines with codes, names, network affiliations, physical modes (metro, bus, tram, RER, rail), commercial modes, colors, text colors, route counts, and operational information. Covers metro systems (RATP Paris, TCL Lyon, TCL Marseille), bus networks, tramway systems, RER lines, Transilien suburban rail, and regional TER services across France. Essential for transit network exploration, line identification, route planning context, network analysis, and understanding service coverage by mode type. AI agents should use this when users ask "list all metro lines in Paris", "show me all tram lines in Lyon", or need line metadata to understand transit network structure and operator affiliations. List all transit lines in a coverage region

get_nearby_stops

Returns nearby objects sorted by distance with coordinates, names, types (stop point, stop area, station, address, POI), distances from search point, served lines, and administrative information. Essential for location-based transit discovery, "stops near me" features, geographic transit analysis, multimodal connection identification, and traveler navigation. AI agents use this when users ask "what metro stations are near my current location", "find transit stops within 500m of these coordinates", or need to discover accessible transit options from a specific geographic point. Supports filtering by object type (stop_point, stop_area, poi, address) and adjustable search radius. Find transit stops near a geographic coordinate

get_networks

Returns network information including names, codes, contributing authorities, coverage areas, associated lines and routes, and operational status. Covers major operators like RATP (Paris metro/bus/tram), SNCF (RER/Transilien/TER), TCL (Lyon), RTM (Marseille), TCL (Toulouse), and dozens of regional and local operators across France. Essential for operator research, network scoping, regional transit analysis, and understanding service governance structure. AI agents should reference this when users ask "what operators run transit in Paris", "list all networks in Ile-de-France", or need to identify transit operators for a specific region before querying lines or disruptions. List all transit operators and networks in a coverage region

get_stop_schedule

Returns all scheduled departures with routes, destinations, first and last departure times, service frequency, headway signatures (days of operation), and physical/commercial mode information. Shows complete timetable structure including weekday, weekend, and holiday service patterns. Essential for comprehensive schedule analysis, journey planning at specific times, timetable visualization, and understanding service frequency throughout the day. AI agents should use this when users ask "show me the full timetable for this metro station", "what times does this bus run on Sundays", or need complete schedule data for a transit stop. Supports depth parameter to control level of detail in route and destination information. Get full timetable for a specific transit stop

plan_journey

Supports combining public transit (metro, bus, tram, regional trains, high-speed rail), walking, cycling, car, bike-sharing (Vélib), and ridesharing. Returns complete itineraries with departure and arrival times, total duration, number of transfers, detailed legs with mode types, line names, operators, intermediate stops, walking distances, real-time disruption alerts, accessibility information (wheelchair access), and fare estimates. Essential for travel planning, multimodal route comparison, passenger information systems, and Mobility-as-a-Service applications across France and European cities. AI agents should use this when users ask "how do I get from Gare du Nord to Eiffel Tower", "plan a trip from Lyon Part-Dieu to Marseille", or need multimodal journey options with timing, transfers, and accessibility details. Supports traveler profiles including wheelchair, slow walker, fast walker, and luggage. Plan a multimodal trip between two locations in France or Europe

search_places

Returns transit stops (stop areas, stop points), stations (metro, tram, bus, rail), addresses, administrative areas, and points of interest with their IDs, names, coordinates, types, and administrative information. Supports autocomplete-style search for journey planning interfaces and location discovery. Essential for stop discovery, address resolution, geocoding, journey origin/destination identification, and building location-based transit features. AI agents should use this when users ask "find the metro station near Champs-Elysees", "search for stops called Republique", or need to identify place IDs and coordinates for use in journey planning queries. Results include embedded links to departures, schedules, and nearby objects for further exploration. Search for transit stops, stations, addresses, and POIs by name

Example Prompts for Navitia in CrewAI

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

"Plan a trip from Gare du Nord to the Eiffel Tower using public transit in Paris."

"Show me all metro departures from Chatelet station in the next 20 minutes."

"What areas can I reach within 45 minutes by public transit from Lyon Part-Dieu station?"

Troubleshooting Navitia MCP Server with CrewAI

Common issues when connecting Navitia 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

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

Navitia + CrewAI FAQ

Common questions about integrating Navitia 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.