Can my AI agent create a complete delivery task with address and recipient?

Yes. Pass a JSON object with destination address, recipient name, phone, and any notes — the agent calls the Onfleet Tasks API and returns the created task with its tracking ID. You can also update or delete tasks the same way.

How does force-completing a task differ from a normal driver completion?

Force-completing bypasses the driver app flow — useful when a delivery was made but the driver forgot to mark it done, or for admin overrides. The agent sends the completion details (success/failure and notes) directly to the Onfleet API, updating the task status without driver interaction.

Can I see which drivers are online right now and their schedules?

Yes. List all workers to see their online/offline status, active task count, and current location. Then query any specific worker's schedule to see their shift times and availability windows. Perfect for capacity planning and real-time dispatch decisions.

Onfleet MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Onfleet
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

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({
        "onfleet": {
            "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 Onfleet, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Onfleet MCP Server

Connect your Onfleet delivery operations to any AI agent and run your fleet from a single conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Onfleet through native MCP adapters. Connect 10 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.

What you can do

Delivery Tasks — Create, update, delete, and force-complete delivery tasks with full address and recipient details
Fleet Tracking — List all active drivers, check who's online, and view their assigned capacities in real time
Driver Schedules — Pull exact shift times and availability windows for any worker in your fleet
Teams & Hubs — Browse your team structure and dispatch hubs with geographic coordinates and zone coverage
Task History — Query tasks by date range to audit completed, failed, or pending deliveries across your operation

The Onfleet MCP Server exposes 10 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 Onfleet to LangChain via MCP

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

Install dependencies

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

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Onfleet via MCP

Why Use LangChain with the Onfleet MCP Server

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

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

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

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

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

Onfleet + LangChain Use Cases

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

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

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

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

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

Onfleet MCP Tools for LangChain (10)

These 10 tools become available when you connect Onfleet to LangChain via MCP:

complete_task_override

Force-complete a delivery task

create_delivery_task

Create a new delivery task in Onfleet

delete_delivery_task

Delete/Archive a delivery task

get_task_details

Get details for a specific delivery task

get_worker_schedule

Get a driver's work schedule

list_dispatch_hubs

List all dispatch hubs

list_fleet_teams

List all delivery teams

list_fleet_workers

List all fleet drivers/workers

list_tasks_by_date

List delivery tasks within a date range

update_delivery_task

Update an existing delivery task

Example Prompts for Onfleet in LangChain

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

"Create a delivery task to 123 Main St, San Francisco for John Doe with phone 415-555-0100."

"Show me all deliveries from yesterday with their status."

"Which drivers are online right now and how many active tasks does each have?"

Troubleshooting Onfleet MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Onfleet + LangChain FAQ

Common questions about integrating Onfleet MCP Server with LangChain.

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.

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.