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

Built by Vinkius GDPR 12 Tools IDE

Cline is an autonomous AI coding agent inside VS Code that plans, executes, and iterates on tasks. Wire TfL through Vinkius and Cline gains direct access to every tool. from data retrieval to workflow automation. without leaving the terminal.

Vinkius supports streamable HTTP and SSE.

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Vinkius Desktop App

The modern way to manage MCP Servers — no config files, no terminal commands. Install TfL and 2,500+ MCP Servers from a single visual interface.

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Classic Setup·json
{
  "mcpServers": {
    "tfl": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}
TfL
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Cline operates autonomously inside VS Code. it reads your codebase, plans a strategy, and executes multi-step tasks including TfL tool calls without waiting for prompts between steps. Connect 12 tools through Vinkius and Cline can fetch data, generate code, and commit changes in a single autonomous run.

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 Cline 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 Cline via MCP

Follow these steps to integrate the TfL MCP Server with Cline.

01

Open Cline MCP Settings

Click the MCP Servers icon in the Cline sidebar panel

02

Add remote server

Click "Add MCP Server" and paste the configuration above

03

Enable the server

Toggle the server switch to ON

04

Start using TfL

Ask Cline: "Using TfL, help me...". 12 tools available

Why Use Cline with the TfL MCP Server

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

01

Cline operates autonomously. it reads your codebase, plans a strategy, and executes multi-step tasks including MCP tool calls without step-by-step prompts

02

Runs inside VS Code, so you get MCP tool access alongside your existing extensions, terminal, and version control in a single window

03

Cline can create, edit, and delete files based on MCP tool responses, enabling end-to-end automation from data retrieval to code generation

04

Transparent execution: every tool call and file change is shown in Cline's activity log for full visibility and approval before committing

TfL + Cline Use Cases

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

01

Autonomous feature building: tell Cline to fetch data from TfL and scaffold a complete module with types, handlers, and tests

02

Codebase refactoring: use TfL tools to validate live data while Cline restructures your code to match updated schemas

03

Automated testing: Cline fetches real responses from TfL and generates snapshot tests or mocks based on actual payloads

04

Incident response: query TfL for real-time status and let Cline generate hotfix patches based on the findings

TfL MCP Tools for Cline (12)

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

01

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

02

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

03

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

04

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

05

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

06

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

07

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

08

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

09

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

10

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

11

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)

12

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

Example Prompts for TfL in Cline

Ready-to-use prompts you can give your Cline agent to start working with TfL immediately.

01

"When is the next Northern Line train arriving at Bank station?"

02

"How many Santander Cycles are available near Hyde Park Corner?"

03

"What is the status of the Victoria Line and Jubilee Line right now?"

Troubleshooting TfL MCP Server with Cline

Common issues when connecting TfL to Cline through the Vinkius, and how to resolve them.

01

Server shows error in sidebar

Click the server name to see logs. Verify the URL and token are correct.

TfL + Cline FAQ

Common questions about integrating TfL MCP Server with Cline.

01

How does Cline connect to MCP servers?

Cline reads MCP server configurations from its settings panel in VS Code. Add the server URL and Cline discovers all available tools on initialization.
02

Can Cline run MCP tools without approval?

By default, Cline asks for confirmation before executing tool calls. You can configure auto-approval rules for trusted servers in the settings.
03

Does Cline support multiple MCP servers at once?

Yes. Configure as many servers as needed. Cline can use tools from different servers within the same autonomous task execution.

Connect TfL to Cline

Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.