London Transport MCP Server

Created By
anoopt8 months ago
This Model Context Protocol (MCP) server provides AI assistants with access to real-time Transport for London data through a set of automated tools.
Overview

What is London Transport MCP?

London Transport MCP is a Model Context Protocol server that provides AI assistants with access to real-time Transport for London (TfL) data through automated tools.

How to use London Transport MCP?

To use the London Transport MCP, connect it to an AI assistant like Claude Desktop or VS Code GitHub Copilot, and utilize the available tools to access live TfL data.

Key features of London Transport MCP?

  • Get the current status of any TfL line (e.g., Central, Victoria, Piccadilly).
  • Access detailed status information including disruption details for a TfL line.
  • Plan journeys between two locations using the TfL Journey Planner.

Use cases of London Transport MCP?

  1. Check if a tube line is running normally before commuting.
  2. Get detailed information about service disruptions.
  3. Plan optimal routes between London locations.
  4. Provide real-time transport advice for London travel.

FAQ from London Transport MCP?

  • Can I use this MCP server with any AI assistant?

Yes! It is designed to work with AI assistants like Claude Desktop and GitHub Copilot.

  • Do I need an API key to use the London Transport MCP?

Yes, you need to provide a valid TfL API key to access the data.

  • What kind of data can I access?

You can access live status updates for TfL lines, disruption details, and journey planning information.

Server Config

{
  "mcpServers": {
    "london-transport": {
      "command": "npx",
      "args": [
        "london-transport-mcp"
      ],
      "env": {
        "TFL_API_KEY": "your_actual_tfl_api_key_here"
      }
    }
  }
}
Project Info
Created At
8 months ago
Updated At
8 months ago
Author Name
anoopt
Star
-
Language
-
License
-

Recommend Servers

View All
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

5 hours ago
Mnemom

6 hours ago