How do I get a DigitalOcean Personal Access Token?

Log in to your DigitalOcean account, navigate to the **API** section in the sidebar, and under the **Tokens/Keys** tab, click **Generate New Token**. Ensure you select the 'read' or 'write' scopes as needed.

Can the agent reboot a Droplet?

This integration currently focuses on listing and auditing infrastructure resources. Power actions like rebooting or resizing should be managed via the DigitalOcean control panel or CLI.

Is the App Platform supported?

Yes, you can use the list_app_platform_services tool to monitor your applications deployed on DigitalOcean's App Platform and check their current deployment status.

DigitalOcean MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: DigitalOcean
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to DigitalOcean through the Vinkius — pass the Edge URL in the `mcps` parameter and every DigitalOcean 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="DigitalOcean Specialist",
    goal="Help users interact with DigitalOcean effectively",
    backstory=(
        "You are an expert at leveraging DigitalOcean 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 DigitalOcean "
        "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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* 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 DigitalOcean MCP Server

Integrate DigitalOcean, the leading cloud infrastructure provider, directly into your AI workflow. Manage your compute instances (Droplets), monitor block storage and DNS domains, track managed databases, and oversee your App Platform services using natural language.

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

What you can do

Compute Oversight — List and retrieve detailed configuration for all your Droplets, including IP addresses and resource status.
Storage & Networking — Monitor block storage volumes and manage DNS domains directly via chat.
Managed Databases — Track your database clusters (Postgres, MySQL, Redis) and their technical metadata.
App Platform Monitoring — Oversee your deployed applications and their active deployment states across projects.

The DigitalOcean 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 DigitalOcean to CrewAI via MCP

Follow these steps to integrate the DigitalOcean 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 DigitalOcean

Why Use CrewAI with the DigitalOcean MCP Server

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

DigitalOcean + CrewAI Use Cases

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

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

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

DigitalOcean MCP Tools for CrewAI (10)

These 10 tools become available when you connect DigitalOcean to CrewAI via MCP:

get_account_metadata

Retrieve metadata and limits for your DigitalOcean account

get_droplet_details

Get detailed information for a specific Droplet

list_app_platform_services

List all applications deployed on DigitalOcean App Platform

list_block_storage_volumes

List all block storage volumes across your account

list_cloud_firewalls

List all cloud firewalls and their associated rules

list_cloud_projects

List all logical projects used to group resources

list_compute_droplets

List all compute instances (Droplets) in your DigitalOcean account

list_dns_domains

List all DNS domains managed in DigitalOcean

list_managed_databases

List all managed database clusters (Postgres, MySQL, Redis, etc.)

search_droplets_by_name

Search for a Droplet using a name keyword

Example Prompts for DigitalOcean in CrewAI

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

"List all active Droplets and their IP addresses."

"Show me our managed database clusters."

"What is the status of our App Platform services?"

Troubleshooting DigitalOcean MCP Server with CrewAI

Common issues when connecting DigitalOcean 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

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

DigitalOcean + CrewAI FAQ

Common questions about integrating DigitalOcean 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.