How to Use the TfL MCP in CrewAI

Q: How does TfL journey planning work with crewai?

You assign roles: one agent runs getjourney to build the core plan, and a second agent uses getroaddisruptions to vet the entire trip for road issues. The collaboration ensures the final suggested route is both efficient and current.

Q: How does crewai handle finding TfL stops?

A 'Discovery Agent' runs searchstoppoint to find IDs. Then, another agent uses those IDs with getarrivals to provide the real-time data you need for trip timing.

Autonomous planning teams using crewai.

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Create your Vinkius account to connect TfL to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup TfL with CrewAI

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Multi-agent trip comparison.

Assign one agent the role of 'Planner' (using `get_journey`) and another the role of 'Risk Analyst'. The Planner suggests a path from Heathrow to Tower Bridge, while the Risk Analyst runs `get_line_status` on every segment. They collaborate to give the user the safest option. This specialized collaboration models how multiple experts review complex data before giving a final answer.

Coordinated last-mile logistics.

Set up two agents: 'Bike Locator' and 'Journey Guide'. The Bike Locator uses `get_bike_points` to find the nearest dock. The Journey Guide then runs a multi-modal plan from `get_journey`, ensuring the final leg of the trip is covered by available bikes. The crew structure keeps these two specialized tasks running in parallel for maximum efficiency.

Proactive disruption reporting.

You can run a team where Agent A checks general status using `get_line_status` and Agent B focuses solely on driving problems via `get_road_disruptions`. The moderator agent then synthesizes these two reports, giving the user a single, comprehensive view of system reliability. The crew structure is ideal for synthesizing diverse data sources like this MCP Server provides.

Setup guide

Set up TfL 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 TfL tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent TfL 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 TfL. 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 TfL MCP in CrewAI

You assign roles: one agent runs `get_journey` to build the core plan, and a second agent uses `get_road_disruptions` to vet the entire trip for road issues. The collaboration ensures the final suggested route is both efficient and current.

Yes. You can create a specialized 'Status Agent' that exclusively uses `get_line_status`. This agent reports on specific lines like the Victoria Line, giving a quick, reliable assessment of overall service health.

A 'Discovery Agent' runs `search_stop_point` to find IDs. Then, another agent uses those IDs with `get_arrivals` to provide the real-time data you need for trip timing.

It's excellent. You can assign an 'Accessibility Agent' that checks details using `get_stop_point_details` and coordinates the journey plan with other agents to guarantee a step-free path.

The server touches operational status, location metadata (from `get_place_search`), real-time arrivals (`get_arrivals`), and road conditions (`get_road_disruptions`). It's a deep dive into London transport infrastructure.

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