Vinkius
TfL logo
Vinkius
Vinkius runs on Claude Code

How to Use the TfL MCP in Claude Code

Script TfL operations using Claude Code via the MCP Server in CI/CD pipelines.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

TfL MCP on Cursor AI Code Editor MCP Client TfL MCP on Claude Desktop App MCP Integration TfL MCP on OpenAI Agents SDK MCP Compatible TfL MCP on Visual Studio Code MCP Extension Client TfL MCP on GitHub Copilot AI Agent MCP Integration TfL MCP on Google Gemini AI MCP Integration TfL MCP on Lovable AI Development MCP Client TfL MCP on Mistral AI Agents MCP Compatible TfL MCP on Amazon AWS Bedrock MCP Support
MCP Servers — Included with Plan
Vinkius runs on Claude Code

Connect TfL MCP to Claude Code

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

GDPR Included with Plan

Key Capabilities

Fetch real-time transport feeds.

Run `get_arrivals` to get predicted arrival times for any specific TfL stop point. This is ideal for running background jobs that constantly monitor service readiness. The output includes timestamps, line names, and vehicle IDs, making it easy to pipe this data into logging systems or triggering notifications in a CI pipeline.

Check London road conditions via MCP Server.

Execute `get_road_disruptions` from your script. You can filter by specific roads, letting the script check for incidents or planned works affecting delivery routes. The returned severity levels and alternative recommendations let you build robust failure detection logic into your deployment scripts.

Map out transport networks.

Use `get_modes` to list all available transport modes in the TfL network. This is essential for defining the full scope of a service or validating input types before running complex journey calculations. It provides structured data on whether your system needs to handle 'tube,' 'bus,' or 'river' services.

Setup guide

Set up TfL MCP in Claude Code

Prerequisites

  • Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Run the add command

    Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.

  2. 2

    Verify the connection

    Start a Claude Code session and type /mcp to list connected servers. You should see tfl-mcp with a green status indicator.

  3. 3

    Start using tools

    Ask Claude Code something like "Check my latest TfL transactions." It will automatically discover and invoke the available TfL tools.

Terminal
claude mcp add --transport http tfl-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Why Choose Vinkius

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

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 TfL MCP in Claude Code

You run the `get_journey` tool, providing start and end coordinates. Your shell script captures multiple route options, including total duration and CO2 savings metrics, making it perfect for calculating efficiency reports in a pipeline.
Yes. Use `get_line_status` to query the current service state of any specific TfL line (e.g., Victoria Line). If the status is 'Suspended,' your script can immediately fail or execute a fallback plan.
You call `get_bike_points` to list all docking stations. This lets you integrate real-time cycle availability into your CI/CD environment, checking if enough bikes are available at a target location.
Use `get_place_search` with coordinates or keywords. This gives you place IDs, names, and categories—structured data that your script can then use to contextualize other API calls.
The server handles public infrastructure data: vehicle compliance details (ULEZ), road status, and station metadata. It never requires or accesses user credentials or private journey logs.

Start using the TfL MCP today

We host it, we monitor it, we maintain it. You just paste one token.

Built & Managed by Vinkius 30s setup 12 tools

We've already built the connector for TfL. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 12 tools are live and waiting. You're up and running in seconds.

Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
+ other MCP clients

Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.

Zero hosting required Full MCP catalog included Enterprise-grade security Auto-updated by Vinkius

Built, hosted, and secured by Vinkius. You just connect and go.