How to integrate Bestbuy MCP with CrewAI

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Introduction

This guide walks you through connecting Bestbuy to CrewAI using the Composio tool router. By the end, you'll have a working Bestbuy agent that can find top-rated laptops under $1000, list best buy stores near seattle, show reviews for samsung galaxy s24, get product details for sku 6356889 through natural language commands.

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

The Bestbuy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Best Buy account. It provides structured and secure access to product, store, category, and review data, so your agent can perform actions like searching for products, retrieving store information, fetching detailed product specs, and analyzing customer reviews on your behalf.

  • Browse and filter product listings: Instantly fetch Best Buy product catalogs, apply category filters, or sort by price and relevance to help users discover the right items.
  • Retrieve detailed product information: Let your agent pull in-depth specifications, pricing, and availability for any product using its SKU, ensuring accurate recommendations or comparisons.
  • Analyze and summarize customer reviews: Access and summarize customer feedback on products, or drill down into specific reviews to guide purchasing decisions.
  • Explore store locations and details: Find Best Buy store locations near you, retrieve detailed store info, and assist with in-store shopping logistics or pickup arrangements.
  • Navigate product categories and metadata: List, search, and explore Best Buy's extensive product categories, making it easy to organize options or narrow down shopping journeys.

Supported Tools & Triggers

Tools
Get CategoriesTool to retrieve best buy product categories.
Get Category DetailsTool to retrieve detailed information about a best buy category by its id.
Get Product Details by SKUTool to retrieve detailed information about a specific product by sku.
Get ProductsTool to retrieve products from best buy.
Get Review DetailsTool to retrieve detailed information for a specific review by id.
Get Product ReviewsTool to retrieve product reviews.
Get Store DetailsTool to retrieve detailed information about a specific best buy store.
Get Best Buy StoresTool to retrieve a list of best buy stores.

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 Bestbuy 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 Bestbuy 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 Bestbuy MCP URL

Create a Composio Tool Router session for Bestbuy

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

Complete Code

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

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

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Bestbuy MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Bestbuy MCP with Claude Agent SDK

Google ADK

Google ADK

Use Bestbuy MCP with Google ADK

LangChain

LangChain

Use Bestbuy MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Bestbuy MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Bestbuy MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Bestbuy MCP with LlamaIndex

FAQ

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

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

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

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

Used by agents from

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Context
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Agent.ai
Altera
DataStax
Entelligence
Rolai

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