This guide walks you through implementing a Model Context Protocol (MCP) server in Python to create an AI-powered shopping assistant. Using Gradio for the interface, you'll learn how to connect an LLM to real-time product data and user intent.
What is MCP?
The Model Context Protocol is a standard for enabling AI models to interact with external tools and data sources. In this tutorial, we build an MCP server that allows a language model to query a product catalog and assist with purchasing decisions.
Step-by-Step Implementation
1. Set Up the Environment
pip install gradio mcp httpx
2. Create the MCP Server
Define tools that the AI can call, such as search_products and get_product_details. Each tool is a Python function with a clear description for the model.
from mcp import Server, Tool
async def search_products(query: str):
# Query external API or database
return [{"name": "Wireless Headphones", "price": 79.99}]
tools = [
Tool(name="search_products", description="Search for products by keyword", input_schema=...)
]
3. Build the Gradio Interface
Create a chat-like UI where users can ask shopping-related questions.
import gradio as gr
def respond(message, history):
# Process through MCP server and LLM
return "I found these options: ..."
gr.ChatInterface(respond).launch()
4. Connect to an LLM
Use an open-source or API-based model (e.g., GPT-4, Claude). Pass the user query along with the available tools and let the model decide which to call.
Running the Assistant
Start the server and Gradio app, then open the provided URL. Users can ask "Find me noise-cancelling headphones under $100" and the assistant will search, present options, and help narrow down choices.
Key Considerations
- Secure your API keys
- Handle errors gracefully
- Rate-limit external requests
"MCP opens up a world of possibilities for AI assistants to perform real-world tasks." — Developer notes
This project demonstrates how to bridge AI language models with practical tools using MCP and Gradio, creating a useful shopping companion.