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 Pydantic AI 10 tools — connect in under 2 minutes

Name: Onfleet
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Onfleet through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Onfleet "
            "(10 tools)."
        ),
    )

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

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.

Pydantic AI validates every Onfleet tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Onfleet MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Onfleet MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Onfleet integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Onfleet connection logic from agent behavior for testable, maintainable code

Onfleet + Pydantic AI Use Cases

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

Type-safe data pipelines: query Onfleet with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Onfleet tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Onfleet and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Onfleet responses and write comprehensive agent tests

Onfleet MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Onfleet to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Onfleet + Pydantic AI FAQ

Common questions about integrating Onfleet MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Onfleet MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.