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TripGo MCP Server for LangChain 9 tools — connect in under 2 minutes

Built by Vinkius GDPR 9 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect TripGo through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Vinkius supports streamable HTTP and SSE.

python
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "tripgo": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using TripGo, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
TripGo
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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 TripGo MCP Server

What you can do

Connect AI agents to the TripGo platform for intelligent multimodal journey planning:

LangChain's ecosystem of 500+ components combines seamlessly with TripGo through native MCP adapters. Connect 9 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

  • Plan trips combining bus, train, subway, tram, ferry, walking, and cycling
  • Find nearby transit stops by GPS coordinates with distance and route info
  • Search stops by name or address for precise location discovery
  • Get real-time departures and arrivals with live delay estimates
  • Track vehicle positions on the map with real-time GPS data
  • Review route information including all stops and agency details
  • Check stop details with accessibility and amenity information
  • Access global regions covering major cities worldwide

The TripGo MCP Server exposes 9 tools through the Vinkius. Connect it to LangChain 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 TripGo to LangChain via MCP

Follow these steps to integrate the TripGo MCP Server with LangChain.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

03

Run the agent

Save the code and run python agent.py

04

Explore tools

The agent discovers 9 tools from TripGo via MCP

Why Use LangChain with the TripGo MCP Server

LangChain provides unique advantages when paired with TripGo through the Model Context Protocol.

01

The largest ecosystem of integrations, chains, and agents. combine TripGo MCP tools with 500+ LangChain components

02

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

03

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

04

Memory and conversation persistence let agents maintain context across TripGo queries for multi-turn workflows

TripGo + LangChain Use Cases

Practical scenarios where LangChain combined with the TripGo MCP Server delivers measurable value.

01

RAG with live data: combine TripGo tool results with vector store retrievals for answers grounded in both real-time and historical data

02

Autonomous research agents: LangChain agents query TripGo, synthesize findings, and generate comprehensive research reports

03

Multi-tool orchestration: chain TripGo tools with web scrapers, databases, and calculators in a single agent run

04

Production monitoring: use LangSmith to trace every TripGo tool call, measure latency, and optimize your agent's performance

TripGo MCP Tools for LangChain (9)

These 9 tools become available when you connect TripGo to LangChain via MCP:

01

get_arrivals

Returns route names, origins, scheduled vs estimated arrival times, and delays. Use this to track incoming vehicles. Requires stop ID. Get upcoming arrivals to a transit stop

02

get_departures

Returns route names, destinations, scheduled vs estimated departure times, and delays. Use this to check when your next ride arrives. Requires stop ID. Get upcoming departures from a transit stop

03

get_nearby_stops

Returns stop IDs, names, coordinates, routes serving each stop, and distance from search point. Use this to find nearest transit options before planning trips. Find transit stops near a GPS coordinate

04

get_regions

Each region has an ID, name, and coverage area. Use this first to verify your city is covered before planning trips. Supports major cities across North America, Europe, Australia, and Asia. List all available transit regions supported by TripGo

05

get_route_info

Requires route ID. Use this to understand route coverage before planning trips. Get information about a specific transit route

06

get_stop_details

Requires stop ID from nearby stops or search results. Use this to review stop facilities before waiting there. Get detailed information about a specific transit stop

07

get_vehicle_positions

Optionally filter by route ID. Use this for real-time tracking of vehicles on the map. Get real-time vehicle positions for transit vehicles

08

plan_trip

Combines public transport (bus, train, subway, tram, ferry) with walking and cycling. Returns multiple trip options with departure/arrival times, duration, number of transfers, and step-by-step instructions. Optionally specify travel time and preferred transport modes. Plan a multimodal trip between two coordinates

09

search_stops

g., "Times Square", "Main St & 5th Ave"). Returns matching stops with IDs, names, coordinates, routes, and relevance scores. Use this when you know the stop name or intersection but not exact coordinates. Search for transit stops by name or address

Example Prompts for TripGo in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with TripGo immediately.

01

"Plan a trip from Central Station to Opera House using only public transit and walking"

02

"What buses are departing from Stop 12345 in the next 15 minutes?"

03

"Show me all train and bus vehicles currently running on Route 480"

Troubleshooting TripGo MCP Server with LangChain

Common issues when connecting TripGo to LangChain through the Vinkius, and how to resolve them.

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

TripGo + LangChain FAQ

Common questions about integrating TripGo MCP Server with LangChain.

01

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
02

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

Connect TripGo to LangChain

Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.