How to Use the TripGo MCP in LlamaIndex

Q: Can LlamaIndex help with TripGo trip planning?

Yes. You can index the results from plantrip. This means your system won't just plan a trip; it will answer questions about how trips were planned in the past, using the live API data.

Q: How do I use LlamaIndex to check TripGo coverage?

First, run getregions and index that output. This lets you build a searchable knowledge base of all supported regions—North America, Europe, Asia, etc.—so you don't have to remember the list.

Q: If I need to find a stop name using TripGo with LlamaIndex, what do I do?

You run searchstops first. The returned matching stops—with their IDs and names—are then indexed. You can later query this index to retrieve specific information about those locations.

Q: Does TripGo support querying different transport modes in LlamaIndex?

The plantrip tool supports public transit, walking, and cycling. When you index the results, your RAG application can answer questions based on all these multimodal travel options.

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Connect TripGo MCP to LlamaIndex

Create your Vinkius account to connect TripGo 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 TripGo with LlamaIndex

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Indexing Past Transit Data

The `plan_trip` tool generates detailed trip options, including step-by-step instructions and multiple transfer points. By indexing this output with LlamaIndex, you can query past journeys to answer questions like, 'What was the quickest way I got from JFK last Tuesday?' This allows your RAG application to ground answers in actual API data, not just generic documents.

Searching Stop Data via LlamaIndex

`search_stops` lets you find transit stops by name or address. When you run this, the resulting stop IDs, names, and coordinates get indexed into your knowledge base. Later, a user can query the index to ask, 'What are all the possible transfer points near Times Square?' The answer comes from the stored API data.

Retrieving Real-Time Status for LlamaIndex

Use `get_arrivals` or `get_departures` to track incoming and outgoing vehicles, capturing scheduled vs. estimated times and delays. Indexing this keeps a history of transit reliability. You can then build queries that answer questions like, 'What was the average delay on Route 34 last month?'

Setup guide

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

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Common questions about TripGo MCP in LlamaIndex

Yes. You can index the results from `plan_trip`. This means your system won't just plan a trip; it will answer questions about *how* trips were planned in the past, using the live API data.

First, run `get_regions` and index that output. This lets you build a searchable knowledge base of all supported regions—North America, Europe, Asia, etc.—so you don't have to remember the list.

This MCP Server deals with coordinates, route IDs, stop names, and time stamps. When indexing, these specific location and temporal data points become part of your searchable knowledge index.

You run `search_stops` first. The returned matching stops—with their IDs and names—are then indexed. You can later query this index to retrieve specific information about those locations.

The `plan_trip` tool supports public transit, walking, and cycling. When you index the results, your RAG application can answer questions based on all these multimodal travel options.

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