How to Use the Upper Route Planner MCP in LangChain

Build multi-step logistics pipelines with LangChain and the Upper Route Planner MCP Server.

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Connect Upper Route Planner MCP to LangChain

Create your Vinkius account to connect Upper Route Planner to LangChain 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 Upper Route Planner with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate Multi-Step Delivery Planning

You can start by creating a new delivery task using `create_upper_delivery_task`. This initiates the whole process, providing an ID you'll need later. Then, your agent calls `list_upper_drivers` to see which personnel are available for that route.

Deep Dive into Route Logistics

Need specific stop data? You can pull details on a single location using `get_upper_route_stop`. Better yet, get everything about that location including coordinates and services with `get_upper_stop_details`. This gives your agent the granular info it needs to plan.

Managing and Checking Routes

Before anything else, check if the service is up by running `check_upper_status`. Once you're ready, listing all available routes with `list_upper_routes` gives your agent a clear map of what needs doing. It’s quick setup for any complex workflow.

Setup guide

Set up Upper Route Planner MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Upper Route Planner tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "upper-route-planner-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Upper Route Planner transactions"
    })
    print(result["messages"][-1].content)

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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 Upper Route Planner. 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 Upper Route Planner MCP in LangChain

LangChain lets you string together multiple steps. For example, your agent first calls `list_upper_routes` to see what’s scheduled, then uses that list to call `get_upper_stop_details` for every stop. It's a chain reaction of data.

Absolutely. You can build logic where the agent first checks `check_upper_status`, then uses that successful status to confidently call `create_upper_delivery_task`. The whole thing flows naturally.

It handles driver information, route schedules, and specific stop details. You can write a chain that first calls `list_upper_drivers` to get the list of people, and then passes that output to another tool for comparison.

Yes. The MCP Server provides tools designed to optimize routes by minimizing drive time. Your agent can use these functions as steps within a larger decision-making pipeline.

The server touches operational logistics data, specifically delivery routes, driver lists, and stop details. Your agent interacts directly with these live records via the MCP Server.

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