How to Use the TransportAPI MCP in CrewAI

Q: How does CrewAI use TransportAPI Alternative MCP Server for journey planning?

One agent is specialized in routing; it calls planpublictransportjourney. The output feeds directly into the next agent, which then formats and presents the final step-by-step guide to the user.

Q: Can CrewAI compare fares using TransportAPI Alternative MCP Server?

Yes. You assign an Agent the gettrainfares tool. This agent pulls pricing data between two stations, allowing a separate Moderating agent to summarize the cost comparison for the user.

Q: What if I need to find stops near a location with CrewAI and TransportAPI Alternative MCP Server?

You set up two sequential tasks. The first task uses getnearbybusstops based on GPS. The second task takes that list of found spots and compiles a final report, ensuring nothing is missed.

Run autonomous transport operations using CrewAI and TransportAPI Alternative MCP Server.

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Setup TransportAPI with CrewAI

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Autonomous Trip Planning

You need a user to plan a trip? Assign one agent (the Planner) the `plan_public_transport_journey` tool. This agent researches step-by-step routes and times between two locations. A second agent can then take that output and use it to estimate costs with `get_train_fares`. The crew works together, ensuring all data points for the journey are covered.

Investigating Service Details

If a user complains about a specific train service, one agent can use `get_train_service_details` to pull the full calling pattern. Another agent then uses `get_train_station_timetable` to cross-reference the expected schedule against reality. The specialized roles ensure comprehensive data gathering before generating a final report.

Comprehensive Location Lookup

Use this setup when input is vague. The Research agent runs `get_transport_by_postcode` to narrow down the area. Then, the Analysis agent uses that postcode's data with `search_transport_places` to confirm specific station names and types. The crew collaboration handles ambiguity by providing multiple layers of verification.

Setup guide

Set up TransportAPI 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 TransportAPI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent TransportAPI 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 →

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="TransportAPI Analyst",
        goal="Access and analyze TransportAPI data via MCP.",
        backstory="Expert analyst with direct TransportAPI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent TransportAPI 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 →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TransportAPI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about TransportAPI MCP in CrewAI

One agent is specialized in routing; it calls `plan_public_transport_journey`. The output feeds directly into the next agent, which then formats and presents the final step-by-step guide to the user.

Yes. You assign an Agent the `get_train_fares` tool. This agent pulls pricing data between two stations, allowing a separate Moderating agent to summarize the cost comparison for the user.

Absolutely. One specialized Agent handles real-time checks by calling `get_live_bus_departures` or `get_live_train_departures`. The results are immediately passed to the final reporting agent.

You set up two sequential tasks. The first task uses `get_nearby_bus_stops` based on GPS. The second task takes that list of found spots and compiles a final report, ensuring nothing is missed.

This server touches public data only: bus routes, train timetables, live departure times, GPS coordinates, station names, and fare structures. No private user details are needed.

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