How to Use the Navitia MCP in CrewAI

Q: How does a CrewAI agent handle transit disruptions on planned routes?

The routing agent calls planjourney to generate itineraries, while a supervisor agent checks those paths against getdisruptions. If a conflict is found, the supervisor orders the routing agent to find a new path.

Q: Can the crew generate transit accessibility maps?

Yes. An analyst agent can call getisochrone to fetch GeoJSON coordinates for reachable zones, which is then processed by a reporting agent to evaluate regional transit accessibility.

Deploy collaborative agent crews to monitor European transit networks and plan routes using CrewAI and Navitia.

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Connect Navitia MCP to CrewAI

Create your Vinkius account to connect Navitia to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Navitia with CrewAI

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Deploy a multi-agent transit command center with MCP Server

The `get_disruptions` tool flags network delays so your CrewAI agents can coordinate alternative routing. Managing public transit logistics requires specialized division of labor. This collaborative approach keeps your operations running smoothly. The monitor agent flags a delayed train line, and the router agent immediately searches for bus alternatives.

Coordinate regional transit audits with CrewAI

The `get_coverage` tool identifies active transit networks to distribute auditing workloads across your agent crew. Auditing European transit networks requires parsing massive amounts of schedule data. They compile these findings into unified reports. By running queries in parallel across different transit networks, your crew gathers complete operator data much faster.

Automated passenger coordination crews

The `get_arrivals` and `get_departures` tools monitor live train statuses to coordinate passenger pickups. Managing passenger pickups requires tracking live arrivals across multiple stations. Finding the exact pickup spot is handled by a helper agent. Using `get_nearby_stops` and `search_places`, the helper locates nearby landmarks and transit exits.

Setup guide

Set up Navitia MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Navitia tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Navitia Analyst",
    goal="Access and analyze Navitia data via MCP.",
    backstory="Expert analyst with direct Navitia access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Navitia transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Navitia Analyst",
        goal="Access and analyze Navitia data via MCP.",
        backstory="Expert analyst with direct Navitia access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Navitia transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

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Common questions about Navitia MCP in CrewAI

CrewAI agents use shared memory to pass transit IDs and coordinates obtained via the MCP server. For example, a search agent resolves a station name using `search_places`, and passes that exact ID to a scheduling agent to run `get_stop_schedule`.

Yes, you can. You can filter tools during setup so that your routing agent only has access to `plan_journey`, while your monitoring agent is restricted to `get_disruptions`.

The routing agent calls `plan_journey` to generate itineraries, while a supervisor agent checks those paths against `get_disruptions`. If a conflict is found, the supervisor orders the routing agent to find a new path.

Yes. An analyst agent can call `get_isochrone` to fetch GeoJSON coordinates for reachable zones, which is then processed by a reporting agent to evaluate regional transit accessibility.

All coordinate data, station IDs, and transit schedules queried by your CrewAI agents are processed in an ephemeral Vinkius sandbox using this secure MCP server. This architecture guarantees that sensitive travel patterns and location coordinates are never cached or leaked.

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