How to Use the TransportAPI MCP in LlamaIndex

Q: How does LlamaIndex use TransportAPI for journey planning?

LlamaIndex indexes getjourneyplan results. When a user asks 'how do I get from A to B,' the agent retrieves and synthesizes the best multi-modal itinerary based on all previously indexed API data.

Q: Can I use TransportAPI with LlamaIndex to check disruptions?

Yes. By indexing getupdates, your application builds a knowledge base of service alerts. Users can query the status and severity levels for specific lines, even months after the alert was received.

Q: Does TransportAPI support querying multiple locations with LlamaIndex?

You can use searchstops to find many Naptan IDs. Then, index data from arrival/departure tools using these identifiers to create a comprehensive, location-aware knowledge graph.

Ground your RAG apps with LlamaIndex: Indexing real UK transport data for semantic search.

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Connect TransportAPI MCP to LlamaIndex

Create your Vinkius account to connect TransportAPI 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 TransportAPI with LlamaIndex

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Build a knowledge base of transit routes

The `get_rail_route` tool provides all the necessary inputs to index rail connectivity. When you run this, LlamaIndex stores data like available services, typical frequency, and first/last service times for any pair of UK stations. Your agent can then answer highly specific questions—like 'What connections exist between Edinburgh and Glasgow?'—using vector search over the indexed travel data.

Search historical bus schedules

Need to know what a route used to look like? The `get_timetable` tool generates full schedule patterns for specific lines. By indexing this output, you allow users to query past service information or complex scheduling rules. This is useful when building an informational knowledge base that goes beyond simple real-time tracking.

Retrieve station facility details

Use `get_station_info` for a deep dive into any UK rail hub's metadata. You can index data points like accessibility features, ticket machine availability, and even annual entry/exit statistics. This allows your RAG application to answer nuanced questions grounded in the API response, rather than just recalling general knowledge.

Setup guide

Set up TransportAPI 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 TransportAPI 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 TransportAPI tools.",
)
response = await agent.run("List recent TransportAPI data")

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Common questions about TransportAPI MCP in LlamaIndex

LlamaIndex indexes `get_journey_plan` results. When a user asks 'how do I get from A to B,' the agent retrieves and synthesizes the best multi-modal itinerary based on all previously indexed API data.

It provides structured, granular details for travel planning. This includes line names, operator details, total elapsed time, and the specific mode (bus/train/walk) used at every stage.

Yes. By indexing `get_updates`, your application builds a knowledge base of service alerts. Users can query the status and severity levels for specific lines, even months after the alert was received.

You can use `search_stops` to find many Naptan IDs. Then, index data from arrival/departure tools using these identifiers to create a comprehensive, location-aware knowledge graph.

This server handles public transit metadata. It touches non-personal service information, specifically stop names and station addresses (location latitude/longitude).

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