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 CrewAI 10 tools — connect in under 2 minutes

Name: Onfleet
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Onfleet through Vinkius, pass the Edge URL in the `mcps` parameter and every Onfleet tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Onfleet Specialist",
    goal="Help users interact with Onfleet effectively",
    backstory=(
        "You are an expert at leveraging Onfleet tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Onfleet "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Onfleet becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Onfleet tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Onfleet

Why Use CrewAI with the Onfleet MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Onfleet through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Onfleet + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Onfleet for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Onfleet, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Onfleet tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Onfleet against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Onfleet MCP Tools for CrewAI (10)

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

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

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Onfleet + CrewAI FAQ

Common questions about integrating Onfleet MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.