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README.md

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----
-title: Amazon Mcp Server
-emoji: 📈
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 5.47.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: amazon-mcp-server
+app_file: amazon_scraper.py
+sdk: gradio
+sdk_version: 5.47.2
+---
+# Amazon MCP Server
+
+This is a Model Context Protocol (MCP) server for scraping Amazon products and searching for products on Amazon.
+
+## Setup
+
+1. **Clone the repository:**
+   ```bash
+   git clone https://github.com/r123singh/amazon-mcp-server.git
+   ```
+2. **Navigate to the project directory:**
+   ```bash
+   cd amazon-mcp-server
+   ```
+3. **Create a virtual environment:**
+   ```bash
+   python -m venv venv
+   ```
+4. **Activate the virtual environment:**
+   - On Linux/macOS:
+     ```bash
+     source venv/bin/activate
+     ```
+   - On Windows:
+     ```bash
+     venv\Scripts\activate
+     ```
+5. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+6. **No API keys or tokens are required.**
+
+7. **Configure MCP JSON:**
+   Create a `mcp.json` file with:
+   ```json
+   {
+     "mcpServers": {
+       "amazon": {
+         "command": "{PATH_TO_DIRECTORY}\\amazon-mcp-server\\venv\\Scripts\\python.exe",
+         "args": [
+           "{PATH_TO_DIRECTORY}\\amazon-mcp-server\\server.py"
+         ]
+       }
+     }
+   }
+   ```
+   Replace `{PATH_TO_DIRECTORY}` with the absolute path to this directory (use `pwd` or `cd` to get the path).
+
+## Available Tools
+
+The server provides the following tools for interacting with Amazon:
+
+- **Scrape a product:**  
+  `scrape_product(product_url)`  
+  Scrape product details (name, price, image, rating, reviews, availability, description) from a given Amazon product URL.
+
+- **Search for products:**  
+  `search_products(query, max_results)`  
+  Search for products on Amazon by keyword and return a list of results.
+
+## Usage
+
+Once configured, the MCP server can be started using the standard MCP client configuration. The server provides a natural language interface to interact with Amazon through the available tools.
+
+**Example usage:**
+- "Get details for this Amazon product: [product URL]"
+- "Search Amazon for 'wireless headphones', show top 3 results"
+
+## Notes
+
+- No API key or authentication is required.
+- The server scrapes publicly available Amazon product and search pages.
+- For best results, use valid Amazon product URLs and clear search queries.
+
+## Contributing
+
+Contributions are welcome! Please open an issue or submit a pull request.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for details.
+
+
+

amazon_scraper.py

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+# amazon_scraper.py
+# This is an Amazon products scraper compatible with Gradio.
+# It can be run as a standalone Gradio app or its functions can be loaded as tools.
+
+import httpx
+import re
+import gradio as gr
+from bs4 import BeautifulSoup
+from urllib.parse import urlparse
+from typing import List, Dict
+
+# --- Helper Functions for Web Scraping ---
+
+async def fetch_amazon_page(url: str) -> str:
+    """Helper function to fetch Amazon product page"""
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
+        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
+        'Accept-Language': 'en-US,en;q=0.5',
+        'Accept-Encoding': 'gzip, deflate',
+        'Connection': 'keep-alive',
+        'Upgrade-Insecure-Requests': '1',
+    }
+    
+    async with httpx.AsyncClient() as client:
+        response = await client.get(url, headers=headers, timeout=15.0)
+        response.raise_for_status()
+        return response.text
+
+def clean_price(price_text: str) -> str:
+    """
+    Cleans and extracts the numerical price from a string.
+    """
+    if not price_text:
+        return "Price not available"
+    # Find the first occurrence of a currency symbol followed by numbers
+    match = re.search(r'([\$\£\€]?\d[\d,.]*)', price_text)
+    if match:
+        return match.group(1)
+    return "Price not available"
+def extract_product_data(html_content: str, url: str) -> dict:
+    """Extract product information from Amazon page HTML"""
+    soup = BeautifulSoup(html_content, 'html.parser')
+    
+    # Initialize product data
+    product_data = {
+        'name': 'Product name not found',
+        'price': 'Price not available',
+        'image_url': 'Image not found',
+        'rating': 'Rating not available',
+        'reviews_count': 'Reviews not available',
+        'availability': 'Availability not found',
+        'description': 'Description not available',
+        'url': url
+    }
+    
+    try:
+        # Extract product name
+        name_selectors = [
+            '#productTitle',
+            'h1.a-size-large',
+            '.a-size-large.product-title-word-break',
+            'h1[data-automation-id="product-title"]'
+        ]
+        
+        for selector in name_selectors:
+            name_elem = soup.select_one(selector)
+            if name_elem:
+                product_data['name'] = name_elem.get_text().strip()
+                break
+        
+        # Extract price
+        price_selectors = [
+            '.a-price-whole',
+            '.a-price .a-offscreen',
+            '.a-price-range .a-price-range-min .a-offscreen',
+            '.a-price .a-price-symbol + span',
+            '[data-a-color="price"] .a-offscreen'
+        ]
+        
+        for selector in price_selectors:
+            price_elem = soup.select_one(selector)
+            if price_elem:
+                product_data['price'] = clean_price(price_elem.get_text())
+                break
+        
+        # Extract image URL
+        image_selectors = [
+            '#landingImage',
+            '#imgBlkFront',
+            '.a-dynamic-image',
+            '[data-old-hires]'
+        ]
+        
+        for selector in image_selectors:
+            img_elem = soup.select_one(selector)
+            if img_elem:
+                img_url = img_elem.get('src') or img_elem.get('data-old-hires')
+                if img_url:
+                    if img_url.startswith('//'):
+                        img_url = 'https:' + img_url
+                    product_data['image_url'] = img_url
+                    break
+        
+        # Extract rating
+        rating_selectors = [
+            '.a-icon-alt',
+            '[data-hook="rating-out-of-text"]',
+            '.a-icon-star-small .a-icon-alt'
+        ]
+        
+        for selector in rating_selectors:
+            rating_elem = soup.select_one(selector)
+            if rating_elem:
+                rating_text = rating_elem.get_text()
+                rating_match = re.search(r'(\d+\.?\d*)', rating_text)
+                if rating_match:
+                    product_data['rating'] = f"{rating_match.group(1)} out of 5"
+                    break
+        
+        # Extract reviews count
+        reviews_selectors = [
+            '#acrCustomerReviewText',
+            '[data-hook="total-review-count"]',
+            '.a-size-base.s-underline-text'
+        ]
+        
+        for selector in reviews_selectors:
+            reviews_elem = soup.select_one(selector)
+            if reviews_elem:
+                reviews_text = reviews_elem.get_text()
+                reviews_match = re.search(r'(\d+(?:,\d+)*)', reviews_text)
+                if reviews_match:
+                    product_data['reviews_count'] = f"{reviews_match.group(1)} reviews"
+                    break
+        
+        # Extract availability
+        availability_selectors = [
+            '#availability .a-size-medium',
+            '#availability span',
+            '.a-size-medium.a-color-success'
+        ]
+        
+        for selector in availability_selectors:
+            avail_elem = soup.select_one(selector)
+            if avail_elem:
+                product_data['availability'] = avail_elem.get_text().strip()
+                break
+        
+        # Extract description
+        desc_selectors = [
+            '#productDescription p',
+            '#feature-bullets .a-list-item',
+            '.a-expander-content p'
+        ]
+        
+        for selector in desc_selectors:
+            desc_elem = soup.select_one(selector)
+            if desc_elem:
+                product_data['description'] = desc_elem.get_text().strip()
+                break
+                
+    except Exception as e:
+        product_data['error'] = f"Error parsing product data: {str(e)}"
+    
+    return product_data
+
+def extract_search_results(html_content: str, max_results: int) -> list:
+    """Extract product information from Amazon search results"""
+    soup = BeautifulSoup(html_content, 'html.parser')
+    products = []
+    
+    # Find product containers
+    product_containers = soup.select('[data-component-type="s-search-result"]')
+    
+    for container in product_containers[:max_results]:
+        try:
+            product = {
+                'name': 'Product name not found',
+                'price': 'Price not available',
+                'image_url': 'Image not found',
+                'rating': 'Rating not available',
+                'url': 'URL not found'
+            }
+            
+            # Extract product name
+            name_elem = container.select_one('a h2 span')
+            if name_elem:
+                product['name'] = name_elem.get_text().strip()
+            
+            # Extract product URL
+            url_elem = container.select_one('a')
+            if url_elem:
+                product_url = url_elem.get('href')
+                if product_url:
+                    if product_url.startswith('/'):
+                        product_url = 'https://www.amazon.com' + product_url
+                    product['url'] = product_url
+            
+            # Extract price
+            price_elem = container.select_one('.a-price-whole')
+            if price_elem:
+                product['price'] = clean_price(price_elem.get_text())
+            
+            # Extract image
+            img_elem = container.select_one('img.s-image')
+            if img_elem:
+                img_url = img_elem.get('src')
+                if img_url:
+                    product['image_url'] = img_url
+            
+            # Extract rating
+            rating_elem = container.select_one('.a-icon-alt')
+            if rating_elem:
+                rating_text = rating_elem.get_text()
+                rating_match = re.search(r'(\d+\.?\d*)', rating_text)
+                if rating_match:
+                    product['rating'] = f"{rating_match.group(1)} out of 5"
+            
+            products.append(product)
+            
+        except Exception as e:
+            print(f"Error extracting product data: {str(e)}")
+    
+    return products
+
+# --- Formatting Functions for Display ---
+
+def format_product_details(product: dict) -> str:
+    """Formats a single product's details into a Markdown string."""
+    return (
+        f"## {product.get('name', 'N/A')}\n"
+        f"**Price:** {product.get('price', 'N/A')}\n\n"
+        f"![Product Image]({product.get('image_url', '')})\n\n"
+        f"**URL:** {product.get('url', 'N/A')}"
+    )
+
+def format_search_results(products: list, query: str) -> str:
+    """Formats a list of search results into a single Markdown string."""
+    if not products:
+        return f"No products found for '{query}'."
+    
+    result = f"# Search Results for '{query}'\n\n---\n\n"
+    for product in products:
+        result += (
+            f"### {product.get('name', 'N/A')}\n"
+            f"**Price:** {product.get('price', 'N/A')}\n"
+            f"**URL:** <{product.get('url', 'N/A')}>\n\n---\n\n"
+        )
+    return result
+    
+# --- Gradio Tool Functions ---
+
+async def scrape_product(product_url: str) -> str:
+    """
+    Scrapes product information from a single Amazon product URL.
+    
+    Args:
+        product_url: The full URL of the Amazon product page.
+        
+    Returns:
+        A Markdown formatted string with the product's name, price, image, and URL.
+    """
+    try:
+        parsed_url = urlparse(product_url)
+        if 'amazon' not in parsed_url.netloc:
+            return "Error: Please provide a valid Amazon product URL."
+            
+        html_content = await fetch_amazon_page(product_url)
+        product_data = extract_product_data(html_content, product_url)
+        return format_product_details(product_data)
+        
+    except httpx.HTTPStatusError as e:
+        return f"HTTP Error: {e.response.status_code}. Amazon may have blocked the request."
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+async def search_products(query: str, max_results: int = 5) -> str:
+    """
+    Searches for products on Amazon and returns a list of results.
+    
+    Args:
+        query: The search term (e.g., "laptop stand").
+        max_results: The maximum number of results to return.
+        
+    Returns:
+        A Markdown formatted string with the search results.
+    """
+    try:
+        search_url = f"https://www.amazon.com/s?k={query.replace(' ', '+')}"
+        html_content = await fetch_amazon_page(search_url)
+        products = extract_search_results(html_content, max_results)
+        return format_search_results(products, query)
+        
+    except Exception as e:
+        return f"An error occurred during search: {str(e)}"
+
+# --- Gradio Interface (for standalone execution) ---
+
+if __name__ == "__main__":
+    print("Starting Amazon Scraper Gradio App...")
+    
+    with gr.Blocks(theme=gr.themes.Soft(), title="Amazon Scraper") as demo:
+        gr.Markdown("# 🤖 Amazon Product Scraper")
+        gr.Markdown("Use the tools below to search for products or scrape a specific product URL.")
+        
+        with gr.Tabs():
+            with gr.TabItem("Search Products"):
+                with gr.Row():
+                    search_query_input = gr.Textbox(label="Search Query", placeholder="e.g., mechanical keyboard")
+                    max_results_input = gr.Number(label="Max Results", value=5, step=1, minimum=1, maximum=20)
+                search_button = gr.Button("Search", variant="primary")
+                search_output = gr.Markdown(label="Search Results")
+
+            with gr.TabItem("Scrape Product by URL"):
+                url_input = gr.Textbox(label="Amazon Product URL", placeholder="Paste a full Amazon URL here...")
+                scrape_button = gr.Button("Scrape", variant="primary")
+                scrape_output = gr.Markdown(label="Product Details")
+        
+        search_button.click(
+            fn=search_products,
+            inputs=[search_query_input, max_results_input],
+            outputs=search_output
+        )
+        
+        scrape_button.click(
+            fn=scrape_product,
+            inputs=[url_input],
+            outputs=scrape_output
+        )
+        
+    demo.launch(mcp_server=True, share=True)

mcp.json

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+{
+  "mcpServers": {
+    "trello": {
+      "command": "python",
+      "args": ["-m", "mcp.server.fastmcp", "server.py"],
+      "env": {
+        "PYTHONPATH": "."
+      }
+    }
+  }
+} 

pyproject.toml

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+[project]
+name = "amazon-mcp-server"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "gradio[mcp]>=5.47.2",
+]