# How to integrate Retailed MCP with Pydantic AI

```json
{
  "title": "How to integrate Retailed MCP with Pydantic AI",
  "toolkit": "Retailed",
  "toolkit_slug": "retailed",
  "framework": "Pydantic AI",
  "framework_slug": "pydantic-ai",
  "url": "https://composio.dev/toolkits/retailed/framework/pydantic-ai",
  "markdown_url": "https://composio.dev/toolkits/retailed/framework/pydantic-ai.md",
  "updated_at": "2026-05-12T10:24:02.734Z"
}
```

## Introduction

This guide walks you through connecting Retailed to Pydantic AI 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 through natural language commands.
This guide will help you understand how to give your Pydantic AI 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.

## Also integrate Retailed with

- [OpenAI Agents SDK](https://composio.dev/toolkits/retailed/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/retailed/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/retailed/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/retailed/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/retailed/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/retailed/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/retailed/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/retailed/framework/cli)
- [Google ADK](https://composio.dev/toolkits/retailed/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/retailed/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/retailed/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/retailed/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/retailed/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/retailed/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- How to set up your Composio API key and User ID
- How to create a Composio Tool Router session for Retailed
- How to attach an MCP Server to a Pydantic AI agent
- How to stream responses and maintain chat history
- How to build a simple REPL-style chat interface to test your Retailed workflows

## What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.
Key features include:
- Type Safety: Built on Pydantic for automatic data validation
- MCP Support: Native support for Model Context Protocol servers
- Streaming: Built-in support for streaming responses
- Async First: Designed for async/await patterns

## 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

| Tool slug | Name | Description |
|---|---|---|
| `RETAILED_GET_GOAT_PRICES` | Get GOAT Product Prices | Tool to retrieve pricing information for a specific product on GOAT. Use when you need up-to-date size-based pricing. Call after confirming product_id. |
| `RETAILED_GET_STOCK_X_PRODUCT` | Get StockX Product | Tool to retrieve detailed StockX product information, including variant-level data. Use when you need comprehensive metadata from StockX by SKU or URL. Response is structured per variant; aggregate price or inventory metrics across variants only after grouping by variant to avoid distortion. |
| `RETAILED_GET_STOCKX_SEARCH` | StockX Search | Tool to search StockX marketplace for products and pricing information. Use when you have a search term and need up-to-date listings on StockX. Note: SKU identifiers and size labels in results may differ from other marketplaces (e.g., GOAT); normalize these fields before cross-platform price comparisons. |
| `RETAILED_GET_STOCKX_TRENDS` | StockX Trends | Tool to get the latest trending products from StockX. Use when you want to discover current trending items. |
| `RETAILED_GET_USAGE` | Get API Usage | Tool to retrieve current API usage statistics. The only mechanism to check remaining API credits; call proactively before long-running analyses to prevent mid-run quota exhaustion. |
| `RETAILED_SEARCH_PRODUCTS` | Search Products | Search for products in Retailed database matching query criteria. Uses Retailed's granular querying language with support for filtering by name, SKU, brand. SKUs and size labels are not standardized across sources; normalize product IDs and size labels before comparing results across marketplaces. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Retailed MCP server is an implementation of the Model Context Protocol that connects your AI agent to Retailed. It provides structured and secure access so your agent can perform Retailed operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account with an active API key
- Basic familiarity with Python and async programming

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Retailed
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your agent to Composio's API
- USER_ID associates your session with your account for secure tool access
- OPENAI_API_KEY to access OpenAI LLMs
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

What's happening:
- We load environment variables and import required modules
- Composio manages connections to Retailed
- MCPServerStreamableHTTP connects to the Retailed MCP server endpoint
- Agent from Pydantic AI lets you define and run the AI assistant
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
```

### 5. Create a Tool Router Session

What's happening:
- We're creating a Tool Router session that gives your agent access to Retailed tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned session.mcp.url is the MCP server URL that your agent will use
```python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Retailed
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["retailed"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

What's happening:
- The MCP client connects to the Retailed endpoint
- The agent uses GPT-5 to interpret user commands and perform Retailed operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
retailed_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[retailed_mcp],
    instructions=(
        "You are a Retailed assistant. Use Retailed tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
```

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Retailed API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Retailed.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Retailed
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["retailed"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    retailed_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[retailed_mcp],
        instructions=(
            "You are a Retailed assistant. Use Retailed tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Retailed.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())
```

## Conclusion

You've built a Pydantic AI agent that can interact with Retailed through Composio's Tool Router. With this setup, your agent can perform real Retailed actions through natural language.
You can extend this further by:
- Adding other toolkits like Gmail, HubSpot, or Salesforce
- Building a web-based chat interface around this agent
- Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Retailed for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## How to build Retailed MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/retailed/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/retailed/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/retailed/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/retailed/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/retailed/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/retailed/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/retailed/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/retailed/framework/cli)
- [Google ADK](https://composio.dev/toolkits/retailed/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/retailed/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/retailed/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/retailed/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/retailed/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/retailed/framework/crew-ai)

## Related Toolkits

- [Addresszen](https://composio.dev/toolkits/addresszen) - Addresszen is a real-time address autocomplete and verification service. It helps capture accurate, deliverable addresses with instant suggestions and validation.
- [Asin data api](https://composio.dev/toolkits/asin_data_api) - Asin data api gives you detailed, real-time product data from Amazon, including price, rank, and reviews. Perfect for e-commerce pros and data-driven marketers who need instant marketplace insights.
- [Baselinker](https://composio.dev/toolkits/baselinker) - BaseLinker is an all-in-one e-commerce management platform connecting stores, marketplaces, carriers, and more. It streamlines order processing, inventory control, and automates your sales operations.
- [Bestbuy](https://composio.dev/toolkits/bestbuy) - Best Buy is a leading retailer offering APIs for product, store, and recommendation data. Instantly access up-to-date retail insights for smarter shopping and decision-making.
- [Btcpay server](https://composio.dev/toolkits/btcpay_server) - BTCPay Server is a free, open-source, self-hosted Bitcoin payment processor. It lets merchants accept Bitcoin payments directly, cutting out middlemen and boosting privacy.
- [Cdr platform](https://composio.dev/toolkits/cdr_platform) - Cdr platform is an API for purchasing carbon dioxide removal services. It enables businesses to offset emissions by accessing verified carbon removal projects.
- [Cloudcart](https://composio.dev/toolkits/cloudcart) - CloudCart is an e-commerce platform for building and managing online stores. It helps businesses streamline product listings, orders, and customer engagement.
- [Countdown api](https://composio.dev/toolkits/countdown_api) - Countdown API gives you real-time, structured eBay product data, reviews, and seller feedback. Perfect for powering price monitoring, product research, or marketplace analytics workflows.
- [Dpd2](https://composio.dev/toolkits/dpd2) - Dpd2 is a robust email management platform for handling, sorting, and automating email workflows. Streamline your communications and boost productivity with advanced sorting, labeling, and response tools.
- [Finerworks](https://composio.dev/toolkits/finerworks) - FinerWorks is an online platform for fine art and photo printing services. Artists and photographers use it to order custom prints and manage print inventory efficiently.
- [Fingertip](https://composio.dev/toolkits/fingertip) - Fingertip is a business management platform for selling, booking, and customer engagement—all from a single link. It helps businesses streamline operations and connect with customers across social channels.
- [Fraudlabs pro](https://composio.dev/toolkits/fraudlabs_pro) - FraudLabs Pro is an online payment fraud detection service for e-commerce and merchants. It helps minimize chargebacks and revenue loss by detecting and preventing fraudulent transactions.
- [Gift up](https://composio.dev/toolkits/gift_up) - Gift Up! is a digital platform for selling, managing, and redeeming gift cards online. It streamlines promotions and gift card transactions for businesses and their customers.
- [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience.
- [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly.
- [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease.
- [Junglescout](https://composio.dev/toolkits/junglescout) - Junglescout is an Amazon product research and analytics platform for sellers. It delivers sales estimates, competitive insights, and optimization tools to boost your Amazon business.
- [Ko fi](https://composio.dev/toolkits/ko_fi) - Ko-fi is a platform that lets creators receive donations, memberships, and sales from fans. It helps creators monetize their work and grow their audience with minimal friction.
- [Lemon squeezy](https://composio.dev/toolkits/lemon_squeezy) - Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless.
- [Loyverse](https://composio.dev/toolkits/loyverse) - Loyverse is a point-of-sale (POS) platform for small businesses, offering tools for sales, inventory, and customer loyalty. It helps streamline retail operations and boost customer engagement.

## Frequently Asked Questions

### 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 Pydantic AI?

Yes, you can. Pydantic AI 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.

---
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