How to Use the Transport for London MCP in CrewAI

Q: How does Transport for London journey planning work with CrewAI?

CrewAI can assign agents to use getjourney for the core plan, while other agents independently check specific service statuses via getlinestatus. This collaborative approach builds a richer, more reliable answer.

Q: Can I automate checking multiple Transport for London lines with CrewAI?

Absolutely. One agent can be assigned the task of calling getlinestatus using comma-separated IDs to check several key tube lines simultaneously, allowing the crew to proceed only when all services are acceptable.

Q: Does CrewAI handle multiple Transport for London disruption sources?

Yes. Agents can be structured to call both getroaddisruptions for street issues and getlinestatus for rail service disruptions, compiling a complete picture of the city's movement challenges.

Q: How do I get detailed info for bus stops and stations?

You use the 'Discovery Agent' to run searchstop, which returns IDs. The crew then passes those specific IDs into getstopdetails to pull all necessary location context.

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Connect Transport for London MCP to CrewAI

Create your Vinkius account to connect Transport for London 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 Transport for London with CrewAI

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Autonomous Trip Planning Team

The 'Planner' agent uses `get_journey` to figure out the route. The 'Verifier' agent then cross-checks this plan by calling `get_line_detail` and `get_line_routes` to verify that all necessary stations are on the expected path. This specialized crew collaboration ensures every leg of the journey is accounted for, from start to finish. No human intervention needed.

Incident Response Crew

Need to know what's wrong? The 'Monitor' agent checks `get_line_status` first. If delays exist, the 'Reporter' agent runs `get_road_disruptions` and combines that data with specific line information from `get_stop_details`. This role-based specialization lets your crew build a detailed incident report instantly, covering both rail and road issues.

Bike and Bus Asset Management Crew

The 'Asset Scout' agent handles the physical layer. It runs `get_bike_points` to find nearby docking stations and uses `get_arrivals` for bus predictions. The shared memory ensures both pieces of data are presented together. This autonomous operation is perfect for building a single dashboard that shows users exactly what vehicles and bikes are available right now.

Setup guide

Set up Transport for London 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 Transport for London tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Transport for London 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="Transport for London Analyst",
        goal="Access and analyze Transport for London data via MCP.",
        backstory="Expert analyst with direct Transport for London access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Transport for London 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 Transport for London. 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 Transport for London MCP in CrewAI

CrewAI can assign agents to use `get_journey` for the core plan, while other agents independently check specific service statuses via `get_line_status`. This collaborative approach builds a richer, more reliable answer.

Absolutely. One agent can be assigned the task of calling `get_line_status` using comma-separated IDs to check several key tube lines simultaneously, allowing the crew to proceed only when all services are acceptable.

The server touches a variety of real-time operational metrics: bus arrivals, line status (delays/suspensions), road congestion levels, bike availability, and station details. This diverse dataset powers the agents.

Yes. Agents can be structured to call both `get_road_disruptions` for street issues and `get_line_status` for rail service disruptions, compiling a complete picture of the city's movement challenges.

You use the 'Discovery Agent' to run `search_stop`, which returns IDs. The crew then passes those specific IDs into `get_stop_details` to pull all necessary location context.

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