How do I use Online Boutique AI Assistant MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Online Boutique AI Assistant MCP Server list all products in the catalog" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Online Boutique AI Assistant MCP Server

PyPI version Python License: MIT Downloads

Model Context Protocol (MCP) Server for Online Boutique AI Assistant

Expose microservices through the standardized Model Context Protocol, enabling any MCP client to access complete e-commerce functionality.

📦 Available on PyPI

Features

Complete E-commerce: 18 microservice functions for products, cart, checkout, payments, shipping
Standard MCP Protocol: Works with any MCP client (Claude, ChatGPT, custom tools)
Google ADK Integration: Built using Google Agent Development Kit patterns
Dynamic Configuration: Environment variable based configuration
Production Ready: Comprehensive logging and error handling

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────────┐
│   MCP Client    │────│  MCP Server      │────│  Microservices      │
│ (Any LLM/Agent) │    │ (This Package)   │    │ (Online Boutique)   │
└─────────────────┘    └──────────────────┘    └─────────────────────┘

Installation

Install from PyPI:

pip install ai-boutique-assit-mcp

Or install from source:

git clone https://github.com/arjunprabhulal/ai-boutique-assit-mcp.git
cd ai-boutique-assit-mcp
pip install -e .

Usage

1. Start MCP Server

The server supports two modes of operation:

HTTP Mode (Web/API Access)

# Standalone HTTP server (default)
boutique-mcp-server --port 8080

# Or explicitly force HTTP mode
boutique-mcp-server --http --port 8081

Stdio Mode (ADK Integration)

# Force stdio mode for direct ADK integration
boutique-mcp-server --stdio

# ADK will automatically launch in stdio mode when using StdioConnectionParams

Available Options

boutique-mcp-server --help

# Options:
#   --port PORT    Port for HTTP mode (default: 8080)
#   --stdio        Force stdio mode (for ADK integration)
#   --http         Force HTTP mode (for web/API access)

2. Connect with ADK Agent

HTTP Connection (Manual Server Start)

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset, SseConnectionParams

agent = Agent(
    name="boutique_assistant",
    model="gemini-2.0-flash",
    instruction="You are a helpful e-commerce assistant.",
    tools=[
        McpToolset(
            connection_params=SseConnectionParams(
                url="http://localhost:8081/mcp"
            )
        )
    ]
)

Stdio Connection (Automatic Server Launch)

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset, StdioConnectionParams, StdioServerParameters

agent = Agent(
    name="boutique_assistant", 
    model="gemini-2.0-flash",
    instruction="You are a helpful e-commerce assistant.",
    tools=[
        McpToolset(
            connection_params=StdioConnectionParams(
                server_params=StdioServerParameters(
                    command="boutique-mcp-server",
                    args=["--stdio"],
                    env={
                        "PRODUCT_CATALOG_SERVICE": "localhost:3550",
                        "CART_SERVICE": "localhost:7070",
                        # Add other service endpoints as needed
                    }
                )
            )
        )
    ]
)

Available Functions

The MCP server exposes 18 e-commerce functions:

Products & Catalog

list_products() - Browse all products
search_products(query) - Search product catalog
get_product(product_id) - Get product details
get_product_with_image(product_id) - Product with image
filter_products_by_price(max_price_usd) - Price filtering

Shopping Cart

add_item_to_cart(user_id, product_id, quantity) - Add to cart
get_cart(user_id) - View cart contents
empty_cart(user_id) - Clear cart

Checkout & Orders

place_order(user_id, currency, address, email, credit_card) - Complete purchase
initiate_checkout() - Start checkout process

Shipping & Logistics

get_shipping_quote(address, items) - Calculate shipping
ship_order(address, items) - Arrange shipping

Payment & Currency

charge_card(amount, credit_card) - Process payment
get_supported_currencies() - Available currencies
convert_currency(from_amount, to_currency) - Currency conversion

Communication

send_order_confirmation(email, order) - Email confirmations

Marketing

get_ads(context_keys) - Promotional content
list_recommendations(user_id, product_ids) - Product suggestions

Configuration

Environment Variables

The server connects to Online Boutique microservices using these default endpoints (Kubernetes service names):

# Default endpoints (production/GKE environment)
PRODUCT_CATALOG_SERVICE="productcatalogservice:3550"
CART_SERVICE="cartservice:7070"
RECOMMENDATION_SERVICE="recommendationservice:8080"
SHIPPING_SERVICE="shippingservice:50051"
CURRENCY_SERVICE="currencyservice:7000"
PAYMENT_SERVICE="paymentservice:50051"
EMAIL_SERVICE="emailservice:5000"
CHECKOUT_SERVICE="checkoutservice:5050"
AD_SERVICE="adservice:9555"

For local testing, override with localhost endpoints:

export PRODUCT_CATALOG_SERVICE="localhost:3550"
export CART_SERVICE="localhost:7070"
export RECOMMENDATION_SERVICE="localhost:8080"
export SHIPPING_SERVICE="localhost:50051"
export CURRENCY_SERVICE="localhost:7000"
export PAYMENT_SERVICE="localhost:50052"
export EMAIL_SERVICE="localhost:5000"
export CHECKOUT_SERVICE="localhost:5050"
export AD_SERVICE="localhost:9555"

Development

Local Development

# 1. Clone the repository
git clone https://github.com/arjunprabhulal/ai-boutique-assit-mcp.git
cd ai-boutique-assit-mcp

# 2. Install dependencies
pip install -r requirements.txt

# 3. Start MCP server
boutique-mcp-server --port 8081

# Or use Python module directly
python -m ai_boutique_assit_mcp.mcp_server --port 8081

# 4. Test with ADK (stdio mode)
adk run your_agent.py

# 5. Test with ADK (HTTP mode - start server first)
boutique-mcp-server --http --port 8081
# Then in another terminal: adk run your_agent.py

Build and Publish

# Build package
python -m build

# Publish to PyPI
python -m twine upload dist/*

Requirements

Python: 3.9 or higher
Google ADK: For MCP integration
gRPC: For microservice communication
Target microservices: Compatible gRPC services

Use Cases

AI Agents: Connect any LLM to e-commerce microservices
API Gateway: Unified access to distributed services
Testing: Mock or test e-commerce workflows
Integration: Standard protocol for microservice access
Multi-platform: Use from Python, Node.js, any MCP client

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Repository: https://github.com/arjunprabhulal/ai-boutique-assit-mcp

Fork the repository
Create your feature branch
Commit your changes
Push to the branch
Create a Pull Request

License

MIT License - see LICENSE file for details.

Online Boutique AI Assistant MCP Server

Online Boutique AI Assistant MCP Server

Table of Contents

Features

Architecture

Installation

Usage

1. Start MCP Server

HTTP Mode (Web/API Access)

Stdio Mode (ADK Integration)

Available Options

2. Connect with ADK Agent

HTTP Connection (Manual Server Start)

Stdio Connection (Automatic Server Launch)

Available Functions

Products & Catalog

Shopping Cart

Checkout & Orders

Shipping & Logistics

Payment & Currency

Communication

Marketing

Configuration

Environment Variables

Development

Local Development

Build and Publish

Requirements

Use Cases

Contributing

License

Resources

Latest Blog Posts

MCP directory API