How to integrate Maintainx MCP with CrewAI

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Introduction

This guide walks you through connecting Maintainx to CrewAI using the Composio tool router. By the end, you'll have a working Maintainx agent that can create a new urgent work order for hvac, list all open work orders at warehouse, add comment to work order 12345, show all assets assigned to maintenance team through natural language commands.

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

The Maintainx MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Maintainx account. It provides structured and secure access to your maintenance operations, so your agent can create work orders, update existing tasks, manage assets, and keep your teams aligned—automatically and on your behalf.

  • Work order creation and management: Instantly have your agent create new work orders, add detailed descriptions, set priorities, and assign them to the right teams or users.
  • Automated work order updates and comments: Let your agent update existing work orders or add comments for documentation and real-time communication between team members.
  • Asset and location tracking: Effortlessly list and retrieve all assets and locations across your organization, helping you keep maintenance data organized and accessible.
  • Category and team organization: Enable your agent to fetch and manage categories or teams, ensuring work orders and assets are classified and assigned correctly.
  • Smart work request portals: Have your agent generate custom work request portals so stakeholders can submit maintenance requests easily and securely.

Supported Tools & Triggers

Tools
Create Work OrderThis tool creates a new work order in maintainx.
Create Work Order CommentThis tool creates a new comment on an existing work order in maintainx.
Create Work Request PortalCreates a new work request portal in maintainx.
Find EntityA tool to search and find specific entities within maintainx by specified fields.
List AssetsThis tool allows users to retrieve a list of all assets in their organization.
List CategoriesThis tool retrieves a list of all categories in your maintainx organization.
List LocationsThis tool retrieves a list of all available locations in the organization's maintainx account.
List TeamsThis tool retrieves a list of all teams in your maintainx organization.
List Work OrdersAction to list work orders from maintainx.
Update Work OrderThis tool allows users to update an existing work order in maintainx by modifying specific attributes without affecting other unchanged fields.
Update Work Order StatusThis tool allows users to update the status of a specific work order in maintainx.

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 Maintainx 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 Maintainx 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 Maintainx MCP URL

Create a Composio Tool Router session for Maintainx

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

Complete Code

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

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

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Maintainx MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Maintainx MCP with Claude Agent SDK

Google ADK

Google ADK

Use Maintainx MCP with Google ADK

LangChain

LangChain

Use Maintainx MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Maintainx MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Maintainx MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Maintainx MCP with LlamaIndex

FAQ

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

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

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

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

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