How to Use the TransportAPI MCP in CrewAI

Q: Can CrewAI check for rail services?

Yes. You can task a specialized agent with checking getrailservices or using getstationinfo to gather all necessary details about train connectivity at any given UK station.

Automate complex travel research with CrewAI and TransportAPI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect TransportAPI MCP to CrewAI

Create your Vinkius account to connect TransportAPI 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.

GDPR Free for Subscribers

Setup TransportAPI with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Researching optimal routes

Need to know the best way from Piccadilly to Old Trafford? Assign one agent to use `get_journey_plan`. This task requires combining multiple data streams—bus, rail, and walking. The CrewAI structure allows a second agent to analyze the results for cost or time. The MCP Server provides all the underlying connectivity details, ensuring the final plan is accurate and comprehensive.

Monitoring station activity

You can assign an 'Operations Agent' to monitor a specific hub. This agent uses `get_rail_departures` or `get_bus_departures` to gather current data points. A second 'Reporting Agent' then compiles this raw data into a human-readable summary. The specialization of agents makes the final output structured and easy for end users to consume.

Identifying local stops

If an agent only knows a general area, it needs specific location context. Use `search_stops` first to find potential Naptan IDs. Then, another agent can use that ID with `get_stop_info` to understand the stop's type and accessibility features. The workflow handles this two-step discovery process automatically, making your operation reliable.

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect TransportAPI now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about TransportAPI MCP in CrewAI

You pass the MCP URL to a specialized agent within your crew. This allows the entire group of agents to access and collaborate using tools like `get_rail_route` or `get_timetable` for deep research.

It automates multi-step decision making. For instance, a crew might first check global disruptions (`get_updates`), and if nothing is found, proceed to plan the journey using `get_journey_plan`.

The server touches every facet of UK transit infrastructure. This includes schedules, real-time arrival/departure times, service disruption alerts, and detailed station metadata. The exact data type is comprehensive facility metadata.

Yes. You can task a specialized agent with checking `get_rail_services` or using `get_station_info` to gather all necessary details about train connectivity at any given UK station.

The API serves as the shared knowledge base for your multi-agent team. It gives agents access to live, reliable data streams about buses and trains across Great Britain, ensuring their collective decisions are grounded in fact.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript