How to integrate Repairshopr MCP with CrewAI

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Introduction

This guide walks you through connecting Repairshopr to CrewAI using the Composio tool router. By the end, you'll have a working Repairshopr agent that can list all upcoming appointments for today, fetch all assets linked to a customer, show attachments for a specific service case, delete an invoice by its unique id through natural language commands.

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

The Repairshopr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Repairshopr account. It provides structured and secure access to your repair shop management system, so your agent can perform actions like managing customer records, handling appointments, viewing assets, retrieving attachments, and organizing contacts on your behalf.

  • Effortless appointment management: Instantly retrieve details of specific appointments, get upcoming schedules, or delete canceled slots directly through your agent.
  • Comprehensive customer and contact handling: Let your agent fetch lists of customers or contacts, update records, or permanently remove outdated customer information for streamlined CRM workflows.
  • Asset tracking and lookup: Quickly search for assets, confirm asset details, or filter assets by customer or status, making it easy to keep tabs on all equipment under management.
  • Service case and attachment retrieval: Have your agent pull all attachments linked to a specific service case, ensuring quick access to important files and documentation.
  • Estimate and invoice cleanup: Empower your agent to delete estimates or invoices that are no longer needed, helping you maintain a tidy, organized business record system.

Supported Tools & Triggers

Tools
Delete AppointmentTool to delete a specific appointment by its id.
Delete CustomerTool to delete a specific customer by id.
Delete EstimateTool to delete a specific estimate by id.
Delete InvoiceTool to delete a specific invoice by id.
Get AppointmentTool to retrieve details of a specific appointment by its id.
Get AppointmentsTool to retrieve a list of appointments.
Get AssetTool to retrieve details of a specific asset by its id.
Get AssetsTool to retrieve a paginated list of assets.
Get Case AttachmentsTool to retrieve attachments for a specific service case.
Get ContactsTool to retrieve a paginated list of contacts.
Get CustomerTool to retrieve details of a specific customer by id.
Get CustomersTool to retrieve a list of customers.
Get Employee Time ClockTool to retrieve the last time clock entry for a specific user.
Get EstimateTool to retrieve details of a specific estimate by id.
Get EstimatesTool to retrieve a list of estimates.
Get InvoiceTool to retrieve details of a specific invoice by id.
Get InvoicesTool to retrieve a paginated list of invoices.
Get LeadTool to retrieve details of a specific lead by its id.
Get LeadsTool to retrieve a paginated list of leads.
Get PaymentTool to retrieve details of a specific payment by id.
Get PaymentsTool to retrieve a paginated list of payments.
Get ProductTool to retrieve details of a specific product by id.
Get ProductsTool to retrieve a list of products.
Get Products By CategoryTool to retrieve products filtered by category id.
Get Product CategoriesTool to retrieve product categories.
Get Product SerialsTool to retrieve all serial numbers for a specific product.
Get TicketTool to retrieve details of a specific ticket by its id.
Get UserTool to retrieve details of a specific user by id.
Get UsersTool to retrieve a list of all users.
Create AppointmentTool to create a new appointment.
Create AssetTool to create a new asset.
Create CustomerTool to create a new customer.
Create EstimateTool to create a new estimate.
Create InvoiceTool to create a new invoice.
Create LeadTool to create a new lead.
Create PaymentTool to create a new payment.
Create ProductTool to create a new product in inventory.
Add Product PhotoTool to add photo(s) to a specific product.

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 Repairshopr 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 Repairshopr 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 Repairshopr MCP URL

Create a Composio Tool Router session for Repairshopr

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

Complete Code

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

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

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Repairshopr MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Repairshopr MCP with Claude Agent SDK

Google ADK

Google ADK

Use Repairshopr MCP with Google ADK

LangChain

LangChain

Use Repairshopr MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Repairshopr MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Repairshopr MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Repairshopr MCP with LlamaIndex

FAQ

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

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

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

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

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