How to integrate Digital ocean MCP with CrewAI

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Digital ocean Logo
CrewAI Logo
divider

Introduction

This guide walks you through connecting Digital ocean to CrewAI using the Composio tool router. By the end, you'll have a working Digital ocean agent that can spin up a droplet for staging environment, provision a new postgresql database cluster, create a dns a record for my domain, add my ssh key to all droplets through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Digital ocean account through Composio's Digital ocean MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Digital ocean connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Digital ocean
  • Build a conversational loop where your agent can execute Digital ocean operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Digital ocean MCP server, and what's possible with it?

The Digital ocean MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your DigitalOcean account. It provides structured and secure access to your cloud infrastructure, so your agent can perform actions like creating droplets, managing domains and DNS, provisioning databases, and organizing resources on your behalf.

  • Automated droplet provisioning: Instantly spin up new virtual machines (droplets) by specifying name, region, size, and image to quickly scale your infrastructure.
  • Database and block storage management: Have your agent create managed database clusters or persistent block storage volumes with custom configurations for seamless backend scaling.
  • Domain and DNS record automation: Simplify domain setup and DNS management by letting your agent create new domains and add or update DNS records as needed.
  • Kubernetes and firewall setup: Easily deploy Kubernetes clusters and configure firewalls by defining rules, regions, and node pools—without manual dashboard work.
  • SSH key and resource tagging: Register new SSH keys for secure access or organize your infrastructure with custom tags, making resource management effortless and consistent.

Supported Tools & Triggers

Tools
Create Custom ImageTool to create a new custom image by providing a url to a linux vm image.
Create Database ClusterTool to create a new managed database cluster.
Create New Block Storage VolumeTool to create a new block storage volume.
Create New DomainTool to create a new domain.
Create Domain RecordTool to create a new dns record for a domain.
Create New DropletTool to create a new droplet.
Create New FirewallTool to create a new firewall.
Create New Kubernetes ClusterTool to create a new kubernetes cluster.
Create New SSH KeyTool to create a new ssh key.
Create New TagTool to create a new tag.
Create New VPCTool to create a new vpc.
Delete Block Storage VolumeTool to delete a block storage volume by id.
Delete Database ClusterTool to delete a database cluster by uuid.
Delete DomainTool to delete a domain by name.
Delete Domain RecordTool to delete a dns record by its record id for a domain.
Delete Existing DropletTool to delete a droplet by id.
Delete FirewallTool to delete a firewall by id.
Delete ImageTool to delete a snapshot or custom image by id.
Delete Load BalancerTool to delete a load balancer instance by id.
Delete SSH KeyTool to delete a public ssh key.
Delete TagTool to delete a tag by name.
Delete VPCTool to delete a vpc by its id.
Create New Load BalancerTool to create a new load balancer.
List Domain RecordsTool to list all dns records for a domain.
List All DatabasesTool to list all managed database clusters on your account.
List All DomainsTool to list all domains in your digitalocean account.
List All DropletsTool to list all droplets in your account.
List All FirewallsTool to list all firewalls on your digitalocean account.
List All ImagesTool to list all images available on your account.
List All Kubernetes ClustersTool to list all kubernetes clusters on your account.
List All Load BalancersTool to list all load balancer instances on your account.
List All SnapshotsTool to list all snapshots available on your digitalocean account.
List All SSH KeysTool to list all ssh keys in your account.
List All TagsTool to list all tags in your account.
List All VolumesTool to list all block storage volumes available on your account.
List All VPCsTool to list all vpcs on your account.
List Database OptionsTool to list valid database engine, version, region, and size options.
Retrieve DomainTool to retrieve details about a specific domain by its name.
Retrieve Domain RecordTool to retrieve a specific dns record for a domain by its record id.
Retrieve Existing DropletTool to show information about an individual droplet by id.
Retrieve Existing ImageTool to retrieve information about an image by id or slug.
Retrieve TagTool to retrieve an individual tag by name.
Retrieve VPCTool to retrieve details about a specific vpc by its id.
Tag ResourceTool to tag resources by name.
Untag ResourceTool to untag resources by tag name.
Update Domain RecordTool to update an existing dns record for a domain.
Update VPCTool to update information about a vpc.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Digital ocean connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Digital ocean via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Digital ocean MCP URL

Create a Composio Tool Router session for Digital ocean

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["digital_ocean"],
)
url = session.mcp.url
What's happening:
  • You create a Digital ocean only session through Composio
  • Composio returns an MCP HTTP URL that exposes Digital ocean tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Digital ocean Assistant",
    goal="Help users interact with Digital ocean through natural language commands",
    backstory=(
        "You are an expert assistant with access to Digital ocean tools. "
        "You can perform various Digital ocean operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Digital ocean MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Digital ocean operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Digital ocean related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_digital_ocean_agent.py

Complete Code

Here's the complete code to get you started with Digital ocean and CrewAI:

python
# file: crewai_digital_ocean_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Digital ocean session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["digital_ocean"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Digital ocean assistant agent
    toolkit_agent = Agent(
        role="Digital ocean Assistant",
        goal="Help users interact with Digital ocean through natural language commands",
        backstory=(
            "You are an expert assistant with access to Digital ocean tools. "
            "You can perform various Digital ocean operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Digital ocean operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Digital ocean related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Digital ocean through Composio's Tool Router. The agent can perform Digital ocean operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Digital ocean MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Digital ocean MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Digital ocean MCP with Claude Agent SDK

Google ADK

Google ADK

Use Digital ocean MCP with Google ADK

LangChain

LangChain

Use Digital ocean MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Digital ocean MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Digital ocean MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Digital ocean MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Digital ocean MCP?

With a standalone Digital ocean MCP server, the agents and LLMs can only access a fixed set of Digital ocean tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Digital ocean and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Digital ocean tools.

Can I manage the permissions and scopes for Digital ocean while using Tool Router?

Yes, absolutely. You can configure which Digital ocean scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Digital ocean data and credentials are handled as safely as possible.

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free