digikala-mcp-server

rezashahnazar/digikala-mcp-server

3.3

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A Model Context Protocol (MCP) server for intelligent product discovery on Digikala, Iran's largest e-commerce platform.

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Digikala MCP Server

A Model Context Protocol (MCP) server for intelligent product discovery on Digikala, Iran's largest e-commerce platform.

🎯 Key Features

  • Intelligent Discovery Workflow: Iterative search refinement with query optimization
  • Bilingual Search: Automatic Persian (Farsi) and English query support
  • Simplified Results: Returns only marketable products with clean, structured data
  • Smart Ratings: Star ratings (0-5) only shown when statistically significant (10+ reviews)
  • Comprehensive Tools: Search, details, recommendations, and AI-powered semantic search

💰 Currency Handling

All price inputs and outputs use TOOMAN currency (Iranian common currency).

  • User-facing: All price parameters accept Toomans
  • API conversion: Automatically converts to Rials (1 Tooman = 10 Rials) internally
  • Dual output: Product prices returned in both Tooman and Rial for transparency

Example:

# Input: Category ID and keyword (from get_optimized_keywords_and_categories), Price in Toomans
search_products(category_id=22, keyword="laptop", price_min_tooman=5000000, price_max_tooman=20000000)

# Output: Both currencies (simplified, parsed)
{
  "id": 12345,
  "title_fa": "لپ تاپ ایسوس",
  "selling_price_tooman": 1525000,  # 1.525M Toomans
  "selling_price_rial": 15250000,   # 15.25M Rials
  "rating_stars": 4.3,               # Out of 5 (only if 10+ reviews)
  "rating_count": 156
}

🔍 Discovery Workflow

This server implements an intelligent discovery workflow:

  1. Query Optimization: Use get_optimized_keywords_and_categories with both Persian and English queries
  2. Iterative Search: Execute search_products with category_id and keyword pairs from step 1
  3. Refine Results: Adjust sorting, pagination, or filters based on results
  4. Deep Dive: Use get_product_details for promising candidates
  5. Expand Search: Use get_product_recommendations for similar products
  6. Visual Search: Use search_text_lenz for clothing/accessories with descriptive queries

🛠️ Available Tools

1. get_optimized_keywords_and_categories

Query optimization and category discovery

  • Purpose: Get optimized search keywords and relevant category IDs
  • When to use: ALWAYS call this BEFORE search_products
  • Bilingual: Call with BOTH Persian (Farsi) and English queries separately
  • Parameters:
    • query (string): Single word query (e.g., 'laptop', 'phone', 'shoe')
  • Returns: Optimized keywords, category IDs, and codes

Example:

# Persian query (single word)
result_fa = get_optimized_keywords_and_categories("لپتاپ")
# English query (single word)
result_en = get_optimized_keywords_and_categories("laptop")

# Returns keyword-category pairs like:
# {"optimized_queries": ["laptop", "gaming laptop"], "categories": [{"id": 22, ...}]}

# Use these keyword-category pairs in search_products

2. search_products

Core product search with filtering (Uses Search API V2)

  • Purpose: Search products within a specific category with advanced filters and sorting
  • Returns: 20 simplified, marketable products per page
  • Critical: Call get_optimized_keywords_and_categories FIRST to get category_id
  • Required: Both category_id and keyword (from step 1 keyword-category pairs)
  • Parameters:
    • category_id (int): REQUIRED - Category ID from get_optimized_keywords_and_categories
    • keyword (string): REQUIRED - Search keyword from get_optimized_keywords_and_categories
    • page (int): Page number (default: 1)
    • sort (int): Sort order (default: 1)
      • 1: Relevance (default)
      • 2: Price Low to High
      • 3: Price High to Low
      • 4: Newest
      • 5: Best Selling
      • 6: Most Viewed
      • 7: Highest Rated
    • price_min_tooman (int): Minimum price in Toomans
    • price_max_tooman (int): Maximum price in Toomans
    • discount_min (int): Minimum discount %
    • discount_max (int): Maximum discount %
    • colors (list[int]): Color filter IDs

Returns: Simplified product list with:

  • id, title_fa, title_en, url
  • selling_price_tooman, selling_price_rial
  • rating_stars (0-5, only if 10+ reviews, otherwise None)
  • rating_count, brand, category
  • discount_percent, is_incredible, is_digiplus_jet_eligible

3. get_product_details

Comprehensive product information

  • Purpose: Deep dive into specific products
  • When to use: After finding promising candidates in search results
  • Parameters:
    • product_id (int): Product ID from search results
  • Returns: Parsed product details including:
    • Basic info (title, category, brand, URL)
    • Pricing (with Tooman/Rial)
    • Star ratings (0-5, if 10+ reviews)
    • Expert review description
    • Specifications (title, values)
    • Buy suggestion (count, percent)
    • Customer feedback (recommended %, advantages, disadvantages)
    • Available variants and price range

4. get_product_recommendations

Similar products and alternatives

  • Purpose: Find similar/related products
  • When to use: When a product is close but not exact match
  • Parameters:
    • product_id (int): Reference product ID
    • offset (int, optional): Specific recommendation type
  • Returns: Simplified product list with:
    • Available recommendation tabs (similar products, related categories)
    • Filtered marketable products only
    • Same fields as search_products

5. search_text_lenz

AI-powered semantic search

  • Purpose: Visual/descriptive product search
  • Best for: Clothing, accessories, wearables, shoes
  • Query format: 2-3 word descriptions (e.g., "red summer dress", "black running shoes")
  • Parameters:
    • query (string): Visual/descriptive query
    • page (int): Page number (default: 1)
  • Returns: Simplified product list plus:
    • is_text_lenz_eligible: Whether Text-Lenz was used
    • related_searches: Alternative search suggestions
    • Pagination metadata

Installation

# Install dependencies
uv sync

# Or add to existing project
uv add "mcp[cli]" httpx

Usage

Running the Server

STDIO Transport (for Claude Desktop, Cursor, etc.)
uv run python main.py
Testing with MCP Inspector
uv run mcp dev main.py

Integration with Claude Desktop

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "digikala": {
      "command": "/path/to/local/uv",
      "args": [
          "run",
          "--directory",
          "/path/to/digikala-mcp-server",
          "python",
          "main.py"
        ]
    }
  }
}

📋 Example Usage

Example 1: Complete Discovery Workflow

Find me the best gaming laptop under 50M Toomans

AI Agent Workflow:

  1. Call get_optimized_keywords_and_categories("لپتاپ") (Farsi - single word)
  2. Call get_optimized_keywords_and_categories("laptop") (English - single word)
  3. Use keyword-category pairs from step 1 in search_products:
    • Example: search_products(category_id=22, keyword="gaming laptop", price_max_tooman=50000000, sort=7)
    • Try multiple keywords: "laptop", "gaming laptop", etc. with same category_id
    • Set sort=7 (Highest Rated) or sort=5 (Best Selling)
  4. Review results, maybe try sort=3 (Price High to Low) for premium options
  5. Call get_product_details(product_id) for top 3 candidates
  6. Compare specifications, ratings, and customer feedback

Example 2: Iterative Search Refinement

I want noise-cancelling headphones for under 10M Toomans

AI Agent Workflow:

  1. Optimize: get_optimized_keywords_and_categories("هدفون") (single word)
  2. Optimize: get_optimized_keywords_and_categories("headphone") (single word)
  3. Get keyword-category pairs and search:
    • search_products(category_id=83, keyword="noise cancelling headphones", price_max_tooman=10000000)
  4. If too many results: Add sort=7 (Highest Rated)
  5. If too few results: Try different keywords or sort=5 (Best Selling) or increase price
  6. Review page 2-3 if needed with page=2
  7. Get details for promising products

Example 3: Visual/Descriptive Search

Find me a casual blue shirt for summer

AI Agent Workflow:

  1. Use search_text_lenz("blue casual shirt") - exceptional for clothing
  2. Review results and related searches
  3. Try variations: search_text_lenz("summer blue shirt")
  4. Get details for promising items
  5. Use get_product_recommendations to explore similar styles

Example 4: Finding Alternatives

I like this product but want to see similar options

AI Agent Workflow:

  1. Get current product: get_product_details(product_id)
  2. Get alternatives: get_product_recommendations(product_id)
  3. Review available recommendation tabs
  4. Try different offsets for category neighbors
  5. Get details for interesting alternatives

Development

Project Structure

digikala-mcp-server/
├── main.py              # MCP server implementation
├── tests.py             # Comprehensive test suite
├── pyproject.toml       # Project configuration
├── uv.lock             # Dependency lock file
└── README.md           # This file

Development Approach

This project follows a diffusion-inspired iterative development methodology:

  • Phase 1: Simple working core (query optimization + basic search)
  • Phase 2: Enhanced features (simplified outputs, bilingual support, integrated extraction)
  • Phase 3: Smart filtering (marketable products, star ratings, intelligent defaults)
  • Future: Error handling and production hardening

Key Design Decisions

  1. Simplified Outputs: All tools return parsed, clean data instead of raw API responses
  2. Marketable Products Only: Non-available products are filtered out automatically
  3. Smart Ratings: Star ratings (0-5) only shown when statistically significant (10+ reviews)
  4. Bilingual Support: Emphasis on searching with both Persian and English queries
  5. Integrated Extraction: Product extraction built into search tools, not separate
  6. Intelligent Defaults: Always filters for available stock (has_selling_stock=1)

Testing

Comprehensive Test Suite

Run the full test suite covering all refactored tools:

uv run python tests.py

The test suite validates:

  • ✅ Query optimization (bilingual support)
  • ✅ Simplified search results (marketable products only)
  • ✅ Rating conversion (stars from 0-5)
  • ✅ Pagination (20 items per page)
  • ✅ Filtering and sorting
  • ✅ Parsed product details
  • ✅ Simplified recommendations
  • ✅ Text-Lenz semantic search
  • ✅ Currency conversion (Tooman/Rial)
  • ✅ Edge cases and error handling

Features:

  • Built-in timeout protection (30s per HTTP request)
  • Proper error handling and cleanup
  • Validates all tool outputs are simplified/parsed
  • Exit codes for CI/CD integration
Direct Function Testing

Quick testing of individual functions:

from main import get_optimized_keywords_and_categories, search_products, get_product_details

# Test query optimization (single word queries)
optimized = get_optimized_keywords_and_categories("لپتاپ")
print(f"Optimized keywords: {optimized['optimized_queries']}")
print(f"Categories: {optimized['categories']}")

# Extract keyword-category pair
category_id = optimized['categories'][0]['id']  # e.g., 22
keyword = optimized['categories'][0]['query']   # e.g., "laptop"

# Test search with filters (returns simplified products directly)
results = search_products(
    category_id=category_id,  # REQUIRED - from optimization step
    keyword=keyword,          # REQUIRED - from optimization step
    price_min_tooman=1000000,   # 10M Rials
    price_max_tooman=10000000,  # 100M Rials
    sort=2,  # Price Low to High
)

# Results are already simplified
for product in results['products'][:3]:
    rating = f"{product['rating_stars']}⭐" if product['rating_stars'] else "No rating"
    print(f"{product['title_fa']}: {product['selling_price_tooman']:,} Toomans - {rating}")

# Get detailed info
details = get_product_details(results['products'][0]['id'])
print(f"Specs: {len(details['specifications'])} attributes")
print(f"Customer feedback: {details.get('customer_feedback', {})}")

API Documentation

This server integrates with:

  • Digikala Autocomplete API v1: https://api.digikala.com/v1/autocomplete/
  • Digikala Search API v2: https://api.digikala.com/v2/category/{category_id}/
  • Digikala Product Details API v2: https://api.digikala.com/v2/product/{id}/
  • Digikala Tabular Recommendations API v1: https://api.digikala.com/v1/product/{id}/tabular-recommendation/
  • Digikala Text-Lenz Search API v1: https://api.digikala.com/v1/search/text-lenz/

License

MIT

📊 Product Data Format

All tools return products in this simplified format:

{
    "id": 12345,
    "title_fa": "عنوان فارسی محصول",
    "title_en": "Product English Title",
    "url": "/product/dkp-12345/...",
    "selling_price_rial": 15250000,
    "selling_price_tooman": 1525000,
    "rrp_price_rial": 18000000,
    "rrp_price_tooman": 1800000,
    "discount_percent": 15,
    "rating_stars": 4.3,        # 0-5 scale, None if <10 reviews
    "rating_count": 156,
    "brand": "Samsung",
    "category": "[Mobile,موبایل]",
    "is_incredible": False,
    "is_digiplus_jet_eligible": True
}

Notes:

  • Only marketable products are returned (automatically filtered)
  • rating_stars is None if rating_count < 10 (not statistically significant)
  • Both Tooman and Rial prices always included
  • URL relative to digikala.com

Version

0.2.0 - Refactored with simplified outputs and intelligent discovery workflow

  • Integrated extraction into search tools
  • Bilingual search support
  • Smart rating conversion
  • Parsed/simplified all outputs
  • Intelligent filtering defaults