Can my AI check when the next Tube train is arriving at my station?

Yes! First use `search_stop_point` with the station name (e.g., "Oxford Circus", "King's Cross") to find the stop ID. Then use `get_arrivals` with that stop ID to get real-time arrival predictions with line names, destinations, time to station in minutes, and platform information. This covers all TfL modes including Tube, bus, DLR, Overground, Elizabeth line, tram, river, and cable-car.

How do I plan a journey from Heathrow Airport to central London using public transport?

Use the `get_journey` tool with "Heathrow Airport" as the origin and your destination (e.g., "Oxford Circus", "Tower Bridge"). The TfL Journey Planner will return multiple route options combining Elizabeth line, Piccadilly line, Heathrow Express, and connecting Tube/bus services. Each option includes total duration, fare estimates, walking distances, number of interchanges, and step-by-step instructions with station sequences. You can also specify preferences for accessibility or cycling options

Are there any Tube line closures or major disruptions right now?

Use `get_line_status` with modes="tube" to check all Tube line statuses, or call it without a mode filter for system-wide status across all transport modes. This returns each line's current status (Good Service, Minor Delays, Severe Delays, Part Suspended, Planned Work) with detailed descriptions and reasons. Always check this before planning any London journey to avoid unexpected disruptions.

TfL MCP Server for Mastra AI 12 tools — connect in under 2 minutes

Name: TfL
Availability: InStock
Author: Vinkius

docs.mastra.ai/docs/agents/mcp-tools

Built by Vinkius GDPR 12 Tools SDK

Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect TfL through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

typescript

import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";

async function main() {
  // Your Vinkius token. get it at cloud.vinkius.com
  const mcpClient = await createMCPClient({
    servers: {
      "tfl": {
        url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
      },
    },
  });

  const tools = await mcpClient.getTools();
  const agent = new Agent({
    name: "TfL Agent",
    instructions:
      "You help users interact with TfL " +
      "using 12 tools.",
    model: openai("gpt-4o"),
    tools,
  });

  const result = await agent.generate(
    "What can I do with TfL?"
  );
  console.log(result.text);
}

main();

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About TfL MCP Server

Connect your TfL Unified API London public transport data platform to any AI agent and take full control of real-time Tube and bus tracking, multimodal journey planning, line status monitoring, and Santander Cycles availability through natural conversation.

Mastra's agent abstraction provides a clean separation between LLM logic and TfL tool infrastructure. Connect 12 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.

What you can do

Real-Time Arrivals — Get live arrival predictions for any Tube station, bus stop, DLR, Overground, Elizabeth line, tram, river, or cable-car stop
Stop Point Search — Find any TfL stop point by station name, street, or landmark across all transport modes
Line Status — Check current service status for all Tube lines, bus routes, DLR, Overground, Elizabeth line, and more
Journey Planning — Plan door-to-door multimodal trips combining Tube, bus, DLR, Overground, Elizabeth line, tram, walking, and cycling
Stop Details — Get comprehensive station information including accessibility, fare zones, and step-free access
Santander Cycles — Browse all bike docking stations with real-time availability (bikes and empty docks)
Road Status — Monitor London road conditions, closures, and disruptions for driving planning
Place Search — Discover places and points of interest across London by category
Transport Modes — Explore all available transport modes in the TfL network
Vehicle Compliance — Check ULEZ and Congestion Charge compliance for registered vehicles

The TfL MCP Server exposes 12 tools through the Vinkius. Connect it to Mastra AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect TfL to Mastra AI via MCP

Follow these steps to integrate the TfL MCP Server with Mastra AI.

Install dependencies

Run npm install @mastra/core @mastra/mcp @ai-sdk/openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.ts and run with npx tsx agent.ts

Explore tools

Mastra discovers 12 tools from TfL via MCP

Why Use Mastra AI with the TfL MCP Server

Mastra AI provides unique advantages when paired with TfL through the Model Context Protocol.

Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add TfL without touching business code

Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation

TypeScript-native: full type inference for every TfL tool response with IDE autocomplete and compile-time checks

One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure

TfL + Mastra AI Use Cases

Practical scenarios where Mastra AI combined with the TfL MCP Server delivers measurable value.

Automated workflows: build multi-step agents that query TfL, process results, and trigger downstream actions in a typed pipeline

SaaS integrations: embed TfL as a first-class tool in your product's AI features with Mastra's clean agent API

Background jobs: schedule Mastra agents to query TfL on a cron and store results in your database automatically

Multi-agent systems: create specialist agents that collaborate using TfL tools alongside other MCP servers

TfL MCP Tools for Mastra AI (12)

These 12 tools become available when you connect TfL to Mastra AI via MCP:

get_arrivals

g., 940GZZLUSCL for Oxford Circus Underground, or 490007653 for a bus stop). Returns predicted arrival times, line names, destination stations, time to station in minutes, vehicle IDs, expected arrival timestamps, and service types (tube, bus, dlr, elizabeth-line, overground, tram, river, cable-car). Essential for real-time arrival awareness, passenger waiting time estimation, trip timing, and connection coordination across the entire London transport network. AI agents should reference this when users ask "when is the next Northern Line train at Bank", "show upcoming buses at stop 490007653", or need real-time arrival predictions for any TfL stop point. Stop IDs can be found using search_stop_point. Get real-time arrival predictions for a specific TfL stop point

get_bike_point

Returns dock ID, common name, precise location (latitude, longitude, address), total capacity, current available bikes, current empty docks, installation date, last update timestamp, and operational status. Essential for dock-level bike availability checks, capacity planning, and real-time bike-sharing awareness for specific docking stations. AI agents should use this when users ask "how many bikes are at dock BikePoints_1234", "tell me about the docking station at Hyde Park Corner", or need specific docking station details for bike hire planning. Get detailed information about a specific Santander Cycles docking station

get_bike_points

Returns docking station IDs, common names, geographic coordinates, total bike capacity, number of available bikes, number of empty docks, installation date, and operational status. Covers thousands of docking stations across central London and expanding into outer boroughs. Essential for bike hire planning, dock availability awareness, cycle route planning, and understanding London's bike-sharing network coverage. AI agents should reference this when users ask "where are the nearest bike docking stations", "how many bikes are available at this dock", or need to identify bike hire options for last-mile connectivity. List all Santander Cycles (bike hire) docking stations across London

get_journey

Returns multiple route options combining tube, bus, dlr, overground, elizabeth-line, tram, river, walking, and cycling. Each route includes total duration, walking distance, number of interchanges, fare estimates, CO2 savings, and detailed leg-by-leg instructions with line names, directions, station sequences, and departure/arrival times. Essential for multimodal trip planning, route comparison, accessibility-aware journey selection, and passenger information. AI agents should use this when users ask "how do I get from Paddington to Greenwich", "plan a journey from Heathrow to Tower Bridge", or need door-to-door trip planning across London's transport network. Plan a journey between two locations using TfL transport modes

get_line_status

Returns line IDs, line names, status severity (Good Service, Minor Delays, Severe Delays, Part Suspended, Suspended, Planned Work, Special Service), status descriptions, reason codes, and disruption details. Can query all lines system-wide or filter by specific modes (tube, bus, dlr, overground, tram, river, cable-car, elizabeth-line, national-rail). Essential for service disruption awareness, alternative route planning, passenger communication, and understanding overall TfL reliability. AI agents should reference this when users ask "is the Victoria Line running normally", "what is the status of the Overground", or need to check service reliability before planning London journeys. Get current service status for TfL lines, optionally filtered by mode

get_modes

Returns modes including tube, bus, dlr, overground, elizabeth-line, tram, river, cable-car, national-rail, and walking. Essential for understanding the scope of TfL's multimodal network, mode identification for filtered queries, and transport network analysis. AI agents should reference this when users ask "what transport modes does TfL cover", "list all available modes", or need to understand the full range of London transport options before planning journeys. List all available transport modes in the TfL network

get_place_search

Returns place IDs, names, categories, geographic coordinates, address information, and related links. Can optionally filter by place type (e.g., "TubeStation", "BusStation", "Park", "Museum", "Hospital"). Essential for place discovery, tourist planning, accessibility research, and understanding London's infrastructure. AI agents should use this when users ask "find parks near Westminster", "search for museums in South Bank", or need to identify places and points of interest for comprehensive London trip planning. Search for places and points of interest across London

get_road_disruptions

Returns disruption descriptions, affected road segments, cause types (roadworks, incidents, events, utility works), start and end dates, severity levels, and alternative route recommendations. Can query all disruptions system-wide or filter by specific road. Essential for driving disruption awareness, alternative route planning, delivery logistics, and understanding road reliability. AI agents should reference this when users ask "are there any roadworks on the A4", "what disruptions affect my drive to Heathrow", or need to check road conditions before planning driving journeys in London. Get current road disruptions and closures across London

get_road_status

Returns road IDs, road names, status descriptions, corridor details, and operational information. Can query all roads system-wide or filter by a specific road ID (e.g., "A1", "A40", "A205" South Circular). Essential for driving route planning, road closure awareness, understanding London road network conditions, and commuter driving decisions. AI agents should use this when users ask "what is the status of the A40", "are there any road closures on the North Circular", or need to check road conditions before driving journeys in London. Get current status of London roads, optionally filtered by specific road

get_stop_point_details

Returns stop ID, common name, station type, modes served, geographic coordinates, address, accessibility information (step-free access, lift availability), fare zone, hub station affiliations, and parent/child station relationships. Essential for stop identification, accessibility planning, fare zone awareness, station navigation, and understanding station hierarchy in the TfL network. AI agents should use this when users ask "tell me about King's Cross station", "is this station step-free", or need detailed stop metadata to contextualise transit queries. Get detailed information about a specific TfL stop point

get_vehicle_details

Returns vehicle registration, make, model, compliance status, charge exemptions, and registration dates. Essential for London driving compliance checks, ULEZ awareness, congestion charge planning, and vehicle registration verification. AI agents should use this when users ask "check if vehicle AB12 CDE is ULEZ compliant", "is my car exempt from Congestion Charge", or need to verify vehicle compliance before driving in central London. Get vehicle details for a registered vehicle in London (ULEZ/congestion charge)

search_stop_point

Returns matching stop points with their IDs, common names, modes served (tube, bus, dlr, overground, tram, river, cable-car, elizabeth-line), geographic coordinates (lat/lon), and station hierarchy information. Can optionally filter by transport mode. Essential for stop discovery, journey planning interfaces, stop identification, and building location-based transit features. AI agents should use this when users ask "find the tube station near Covent Garden", "search for stops called Victoria", or need to identify stop IDs for use in arrival queries. Search for TfL stop points by name or location