How to Use the Transport for London MCP in LlamaIndex

Q: Can Transport for London and LlamaIndex analyze historical disruptions?

Yes. By indexing the output of getroaddisruptions, you can build a searchable knowledge base that answers questions about past incidents, severity levels, and locations, which is much more powerful than simple live status checks.

Q: Can I use Transport for London and LlamaIndex to predict resource needs?

Absolutely. By indexing the detailed results from getbikepoints over time, you can create reports that identify patterns in bike or dock usage at specific station locations.

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Connect Transport for London MCP to LlamaIndex

Create your Vinkius account to connect Transport for London to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Transport for London with LlamaIndex

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Indexing TfL Data with LlamaIndex

When you use `get_journey`, LlamaIndex indexes the full results—including multiple route options, fare costs, and step-by-step directions. This output becomes part of your searchable knowledge base, allowing users to query historical or complex journey data semantically. The MCP tool output is no longer just a single API call; it's indexed alongside documents. You can build RAG applications that combine live transport results with static policy manuals for deeper context.

Analyzing TfL Status via LlamaIndex

The `get_line_status` tool reports whether a specific tube line has Good Service or Severe Delays. By indexing this output, you can query past incident reports, asking questions like 'What were the common causes of delays on the Victoria Line in Q3?' The answer is grounded directly in the API data. Similarly, `get_road_disruptions` results are indexed, letting your agent search through records of past road closures and their estimated clearance times.

Bike Availability Tracking with MCP Server

`get_bike_points` returns detailed metrics on dock availability and bike counts. LlamaIndex ingests this data, meaning you can build a knowledge base that tracks trends—for example, 'Which area consistently shows low bike availability during peak hours?' This is crucial for planning cycles. The agent uses the indexed bicycle point data alongside journey results to offer optimized advice.

Setup guide

Set up Transport for London MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Transport for London MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Transport for London tools.",
)
response = await agent.run("List recent Transport for London data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Transport for London. 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 Transport for London MCP in LlamaIndex

LlamaIndex doesn't just execute calls; it indexes the full output of tools like `get_journey`. This means your agent can query past sessions or configurations, getting answers grounded in actual API data rather than relying on temporary memory.

Yes. By indexing the output of `get_road_disruptions`, you can build a searchable knowledge base that answers questions about past incidents, severity levels, and locations, which is much more powerful than simple live status checks.

The core mechanism involves combining the MCP tool output into a unified index. You can mix live API results (like `get_arrivals`) with policy documents or historical records to provide comprehensive answers.

The server handles high-volume, real-time operational metrics: predicted arrival times (from `get_arrivals`), line status codes (from `get_line_status`), and bike availability counts (from `get_bike_points`).

Absolutely. By indexing the detailed results from `get_bike_points` over time, you can create reports that identify patterns in bike or dock usage at specific station locations.

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