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 LlamaIndex 10 tools — connect in under 2 minutes

Name: Onfleet
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Onfleet as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Onfleet. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Onfleet?"
    )
    print(response)

asyncio.run(main())

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* 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.

LlamaIndex agents combine Onfleet tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Onfleet

Why Use LlamaIndex with the Onfleet MCP Server

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

Data-first architecture: LlamaIndex agents combine Onfleet tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Onfleet tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Onfleet, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Onfleet tools were called, what data was returned, and how it influenced the final answer

Onfleet + LlamaIndex Use Cases

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

Hybrid search: combine Onfleet real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Onfleet to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Onfleet for fresh data

Analytical workflows: chain Onfleet queries with LlamaIndex's data connectors to build multi-source analytical reports

Onfleet MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Onfleet to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Onfleet + LlamaIndex FAQ

Common questions about integrating Onfleet MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Onfleet tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.