Universal Shopping Agent MCP Server
Allows searching for products on Amazon India, leveraging AI-generated search terms to find items matching the user's shopping intent.
Allows searching for products on Flipkart India, leveraging AI-generated search terms to find items matching the user's shopping intent.
Uses Google Gemini AI to analyze natural language shopping queries and extract structured intent, including product category, budget, and desired features, which then drives the search on e-commerce platforms.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Universal Shopping Agent MCP ServerFind a smartphone under ₹25,000 on Amazon India"
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.
Universal Shopping Agent MCP Server
A Model Context Protocol (MCP) server that acts as an intelligent shopping assistant, using Google Gemini AI to analyze shopping intent and automate product searches across multiple e-commerce platforms.
Features
AI-Powered Intent Analysis: Uses Google Gemini to extract structured shopping intent from natural language queries
Multi-Platform Support: Searches across Amazon, Flipkart, and Myntra with country-specific domains
Smart Search Optimization: Generates optimized search terms based on analyzed requirements
Automated Browsing: Uses Playwright to automatically open browsers and perform searches
Budget & Feature Filtering: Extracts and applies budget constraints and specific features
Related MCP server: Amazon MCP Server
Supported Platforms & Countries
Amazon: IN (India)
Myntra: IN (India)
Prerequisites
Python 3.9+
uv(The ultra-fast Python package and project manager)Claude Desktop App
Google Gemini API Key
Installation & Setup
Navigate to the project directory:
cd /path/to/this/folderInitialize the project and create a virtual environment:
uv init universal-shopping-agent uv venvCreate a
.envfile with your Gemini API key:GEMINI_API_KEY=your_gemini_api_key_hereInstall the dependencies from the provided
requirements.txt:uv add -r requirements.txtInstall the Playwright browser:
playwright install chromium
Running the Server
To test and run the MCP server locally, use:
uv run --with "mcp[cli]" mcp run main.pyIf it runs without errors, you are ready to connect it to Claude.
Connecting to Claude Desktop
Open Claude Desktop.
Go to Settings -> Developer -> Edit MCP Server Configuration. This will open the
claude_desktop_config.jsonfile.Add a new configuration for this server. Replace the paths with the absolute paths on your system.
{
"mcpServers": {
"universal-shopping-agent": {
"command": "/path/to/your/uv",
"args": [
"run",
"--directory",
"/path/to/your/universal-shopping-agent",
"python",
"main.py"
]
}
}
}command: The absolute path to youruvinstallation. Find it by runningwhich uvin your terminal.args[3](--directory): The absolute path to this project folder.
Save the file and restart Claude Desktop.
Usage Examples
Once configured, you can ask Claude shopping-related questions like:
"My father needs a new smartphone under ₹20,000 with good battery life and clear video calls. Can you find recommendations on Amazon India?"
"I need a college laptop under 40k that can handle online classes and light coding. Search across Indian e-commerce sites."
"Find wireless earbuds under ₹5,000 with good sound quality and 20+ hours battery on Amazon."
Claude will:
Use Gemini AI to analyze your shopping intent
Ask for permission to connect to the shopping agent
Open a Chromium browser to perform the search on the appropriate platform
Return the search results and intent analysis
How It Works
Intent Analysis: Gemini AI extracts structured information from your query (category, budget, features, etc.)
Search Optimization: Generates the best search terms for e-commerce platforms
Automated Browsing: Opens amazon platform and performs the search automatically
Troubleshooting
Gemini API Errors: Ensure your
GEMINI_API_KEYis set correctly in the.envfileBrowser Issues: Make sure Playwright Chromium is installed:
playwright install chromiumPlatform Errors: E-commerce websites frequently change their HTML structure; selectors may need updating
Connection Issues: Verify all paths in your Claude MCP configuration are absolute paths
Important Notes
The server opens a visible browser window (
headless=False) to show you the search resultsSome platforms may show login popups; the code handles common ones like Flipkart's
For clothing items, the agent automatically prefers Myntra over Amazon in India
Always check the actual search results on the platform for the most current prices and availability
Example Output
When you ask about smartphones under ₹20,000, Claude will return:
Structured intent analysis from Gemini
Optimized search terms used
Platform where the search was performed
Confirmation that the browser was opened with your search
Note: This tool is for educational and personal use. Always verify product details and prices on the actual e-commerce platforms before making purchases.
Available Tools
1 toolshop_toysA
Open a browser and search Amazon for age-appropriate toys based on the query.
Example queries:
"I want to order toys for my 1 year old"
"toys for 8 months baby sensory"
country: IN | US | UK | DE (defaults to IN)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| country | No | IN |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool opens a browser, a notable side effect. Also specifies default country behavior and allowed country values. No annotations provided, but description adds meaningful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Highly concise: two sentences plus a code block for country. No redundant content; examples directly support usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with output schema, the description covers purpose and input details adequately. Missing return format but not critical for functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds value beyond schema by listing exact country values and defaults (IN, US, UK, DE). Query parameter is exemplified, though not formally described. Schema coverage 0% so description compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Describes a specific action: searching Amazon for age-appropriate toys using a query. Clearly states the resource (Amazon) and the verb (search). No sibling tools require differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides example queries illustrating when to use (e.g., 'toys for 1 year old'), but no explicit guidance on when not to use or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.0- First observed
shop_toys
TDQS
Scored across 1 tool
With only one tool, there is no ambiguity; the tool's purpose is clearly defined and distinct by default.
The single tool follows a clear verb_noun pattern ('shop_toys'), and consistency is perfect with no other tools to conflict.
The server claims to be a 'Universal Shopping Agent' but only offers one tool for toys, which is far too few for the implied scope, making it feel underwhelming and incomplete.
The tool only handles toy searches on Amazon; there are no tools for other product categories, cart management, or order processing, leaving major gaps in the shopping domain.
Maintenance
Related MCP Connectors
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AI shopping gateway for product search, inventory, carts, and merchant-hosted checkout.
Routes natural-language shopping queries to merchant storefronts, returns normalized results.
AI-agent product catalog: search, lookup & purchase routing over verified merchant data.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to search products, manage shopping carts, place orders, and retrieve order history from Amazon and Target accounts.2MIT
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- AlicenseNot gradedqualityDmaintenanceEnables customer support across e-commerce platforms by providing order management, product guidance, and account assistance tools through natural language queries.5MIT
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