How to integrate Retailed MCP with CrewAI

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Introduction

This guide walks you through connecting Retailed to CrewAI using the Composio tool router. By the end, you'll have a working Retailed agent that can show current goat prices for product id 12345, find trending sneakers on stockx today, get stockx details for sku aq2667-200, search retailed for yeezy boost 350 listings through natural language commands.

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

The Retailed MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retailed account. It provides structured and secure access to your retail data and e-commerce integrations, so your agent can perform actions like product searches, dynamic price retrieval, trend analysis, inventory checks, and API usage monitoring on your behalf.

  • Real-time product search and discovery: Instantly search for products across supported platforms and retrieve detailed information based on your custom criteria.
  • Dynamic pricing and size-based quotes: Ask your agent to pull the latest pricing for specific products and sizes from marketplaces like GOAT and StockX.
  • Trend analysis and market insights: Have the agent surface the latest trending products from StockX, helping you spot opportunities and popular items quickly.
  • Comprehensive product metadata access: Retrieve in-depth product metadata from StockX by SKU or URL for more informed decisions and listings.
  • API usage and quota monitoring: Let your agent track your current API usage statistics, so you stay on top of your account limits and avoid surprises.

Supported Tools & Triggers

Tools
Get GOAT Product PricesTool to retrieve pricing information for a specific product on goat.
Get StockX ProductTool to retrieve detailed stockx product information.
StockX SearchTool to search stockx marketplace for products and pricing information.
StockX TrendsTool to get the latest trending products from stockx.
Get API UsageTool to retrieve current api usage statistics.
Search ProductsSearch for products in retailed matching query criteria.

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 Retailed 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 Retailed 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 Retailed MCP URL

Create a Composio Tool Router session for Retailed

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

Complete Code

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

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

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Retailed MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Retailed MCP with Claude Agent SDK

Google ADK

Google ADK

Use Retailed MCP with Google ADK

LangChain

LangChain

Use Retailed MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Retailed MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Retailed MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Retailed MCP with LlamaIndex

FAQ

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

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

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

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

Used by agents from

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