How do I find my Arrivy API Key?

Log in to your Arrivy account, go to **Settings > Integrations**, and you will find your API Key under the **Developer** or **API** section.

Can I assign a crew to a task via this server?

Yes, you can use the `update_task` tool and provide the crew IDs in the update body to assign or reassign tasks.

Does Arrivy support real-time ETAs?

Yes, Arrivy calculates real-time ETAs based on field personnel location. You can retrieve these via the `get_task` or `list_locations` tools.

Arrivy MCP Server for CrewAI 9 tools — connect in under 2 minutes

Name: Arrivy
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to Arrivy through Vinkius, pass the Edge URL in the `mcps` parameter and every Arrivy 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

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Arrivy Specialist",
    goal="Help users interact with Arrivy effectively",
    backstory=(
        "You are an expert at leveraging Arrivy 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 Arrivy "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

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

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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 Arrivy MCP Server

The Arrivy MCP Server empowers your AI agent to coordinate field operations and last-mile delivery directly from your workspace. Seamlessly manage your mobile workforce, track job progress, and engage with customers using natural language.

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

Key Features

Task Orchestration — List, create, and update service tasks or delivery jobs with real-time status tracking.
Crew Management — Monitor field personnel and resource assignments to ensure efficient job allocation.
Customer Engagement — Manage customer records and sync service history for better communication.
Location Tracking — Access real-time location data and ETAs for your field technicians and delivery drivers.
Digital Workflow — Access data captured in the field, including forms, photos, and status updates.
Seamless Integration — Connect your Arrivy operations with your AI-assisted project management and support workflows.

Benefits for Teams

Operations Managers — Quickly audit active jobs and crew statuses without leaving your AI dashboard.
Dispatchers — Use AI to quickly create and assign new tasks based on customer requests.
Customer Success — Retrieve job history and ETAs instantly to provide accurate updates to clients.

The Arrivy MCP Server exposes 9 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 Arrivy to CrewAI via MCP

Follow these steps to integrate the Arrivy 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 9 tools from Arrivy

Why Use CrewAI with the Arrivy MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Arrivy 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

Arrivy + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Arrivy 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 Arrivy, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Arrivy 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 Arrivy against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Arrivy MCP Tools for CrewAI (9)

These 9 tools become available when you connect Arrivy to CrewAI via MCP:

create_customer

Create a new customer record

create_task

Create a new service task in Arrivy

get_account_check

Verify Arrivy account connection

get_task

Get details for a specific task

list_crews

List all field crews and personnel

list_customers

List all customers in the system

list_locations

List all tracked locations

list_tasks

List all service tasks in Arrivy

update_task

Update an existing service task

Example Prompts for Arrivy in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Arrivy immediately.

"List all scheduled tasks for today in Arrivy."

"Create a new task 'Emergency Leak Repair' at '123 Maple St'."

"Show me the status of task ID 'T12345'."

Troubleshooting Arrivy MCP Server with CrewAI

Common issues when connecting Arrivy 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.

Arrivy + CrewAI FAQ

Common questions about integrating Arrivy 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.