How to integrate Apaleo MCP with CrewAI

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Introduction

This guide walks you through connecting Apaleo to CrewAI using the Composio tool router. By the end, you'll have a working Apaleo agent that can archive a property that's no longer active, clone existing property for new location, create a new unit group for suites, check if a specific unit exists through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Apaleo account through Composio's Apaleo 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 Apaleo connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Apaleo
  • Build a conversational loop where your agent can execute Apaleo 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 Apaleo MCP server, and what's possible with it?

The Apaleo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apaleo account. It provides structured and secure access to your property management operations, so your agent can perform actions like managing properties, handling units, checking availability, and automating setup tasks on your behalf.

  • Property management and archiving: Enable your agent to create, clone, or archive properties, letting you quickly scale or reorganize your portfolio as your business evolves.
  • Unit and unit group operations: Let your agent create new units or unit groups, check if specific units exist, and manage all aspects of your inventory with ease.
  • Attribute and setup automation: Ask your agent to create or verify unit attributes, ensuring your property data is always up-to-date and consistent.
  • Bulk unit creation: Allow your agent to generate multiple units in one go, following custom naming rules, to save you time during onboarding or expansion.
  • Property cloning and rapid deployment: Have your agent clone existing properties with all inventory and rate plans, making it simple to launch new locations based on proven setups.

Supported Tools & Triggers

Tools
Archive a propertyUse this endpoint to archive an existing live property this operation set the isarchived flag to trueyou must have at least one of these scopes: 'properties.
Check if a property existsCheck if a property exists by id.
Check if a unit attribute existsCheck if a unit attribute existsyou must have at least one of these scopes: 'unitattributes.
Check if a unit existsCheck if a unit exists by id.
Check if a unit group existsCheck if a unit group exists by id.
Clones a propertyUse this call to clone a specific property.
Create a unitUse this call to create a new unit.
Create a unit attributeUse this call to create a new unit attribute.
Create a unit groupUse this call to create a new unit group.
Create multiple unitsUse this call to create multiple units, following a naming rule.
Creates a propertyUse this call to create a new property.
Delete a unitUse this call to delete a unit.
Delete a unit groupUse this call to delete a unit group.
Deletes unit attributeDeletes unit attributeyou must have at least one of these scopes: 'unitattributes.
Get a properties listGet the list of properties.
Get a propertyGet a property by id.
Get a unitGet a unit by id.
Get a unit groupGet a unit group by id.
Get a units listGet the list of units.
Get unit attribute by idGet unit attribute by idyou must have at least one of these scopes: 'unitattributes.
Get unit attribute listGet unit attribute listyou must have at least one of these scopes: 'unitattributes.
List Unit GroupsGet the list of unit groups.
Move property to liveUse this endpoint to move an existing test property to live this operation changes the property status to 'live'you must have at least one of these scopes: 'properties.
Replace a unit groupUse this call to modify a unit group.
Reset Property DataThis endpoint deletes transactional data for a property in 'test' status.
Returns a list of supported countriesReturns a list of iso country codes that could be used to create properties.
Returns number of unit groupsReturns number of unit groups matching the filter criteriayou must have at least one of these scopes: 'unitgroups.
Returns number of unitsReturns number of units matching the filter criteriayou must have at least one of these scopes: 'units.
Return total count of propertiesReturn total count of propertiesyou need to be authorized (no particular scope required)

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 Apaleo 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 Apaleo 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 Apaleo MCP URL

Create a Composio Tool Router session for Apaleo

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["apaleo"],
)
url = session.mcp.url
What's happening:
  • You create a Apaleo only session through Composio
  • Composio returns an MCP HTTP URL that exposes Apaleo 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="Apaleo Assistant",
    goal="Help users interact with Apaleo through natural language commands",
    backstory=(
        "You are an expert assistant with access to Apaleo tools. "
        "You can perform various Apaleo 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 Apaleo 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 Apaleo 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 Apaleo 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_apaleo_agent.py

Complete Code

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

python
# file: crewai_apaleo_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 Apaleo session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["apaleo"],
    )
    url = session.mcp.url

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

    # Create Apaleo assistant agent
    toolkit_agent = Agent(
        role="Apaleo Assistant",
        goal="Help users interact with Apaleo through natural language commands",
        backstory=(
            "You are an expert assistant with access to Apaleo tools. "
            "You can perform various Apaleo 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 Apaleo 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 Apaleo 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 Apaleo through Composio's Tool Router. The agent can perform Apaleo 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 Apaleo MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Apaleo MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Apaleo MCP with Claude Agent SDK

Google ADK

Google ADK

Use Apaleo MCP with Google ADK

LangChain

LangChain

Use Apaleo MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Apaleo MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Apaleo MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Apaleo MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Apaleo MCP?

With a standalone Apaleo MCP server, the agents and LLMs can only access a fixed set of Apaleo tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Apaleo 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 Apaleo tools.

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

Yes, absolutely. You can configure which Apaleo 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 Apaleo data and credentials are handled as safely as possible.

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