wildberries-mcp

An MCP server that turns Wildberries (the largest Russian marketplace) into a toolkit for LLM agents. It lets a model search products, inspect a product card, read its price history and reviews, and compare several products side by side — i.e. do cross-product research, which Wildberries' own built-in review AI (single-product only) does not do.

Tools

Tool	What it does
search_products(query, limit)	Search by keyword → article, name, brand, price, rating, review count
get_product(article)	Full card: name, brand, category, description, current price, rating
get_price_history(article)	Price points over time → reason about trends and real discounts
get_reviews(article, limit)	Aggregate rating, star distribution, review counts, sample review texts
compare_products([articles])	Side-by-side comparison of several products

How it works (the interesting part)

Wildberries has no public API for this, so the server talks to the same internal endpoints the website uses — which took some reverse-engineering, because they moved:

• Product data comes from the basket CDN: basket-XX.wbbasket.ru/volA/partB/{article}/info/ru/card.json. The XX host isn't fixed — it's derived from the article and the mapping changes as WB adds shards, so the client probes and caches the right host per volume instead of hardcoding a table.
• Price history lives next to it: .../info/price-history.json.
• Reviews are keyed by imt_id (not the article), so the client reads imt_id from the card, resolves the feedback host via feedback-bt.wildberries.ru, then fetches feedbacks/v2/{imt_id}.
• Search goes through search.wb.ru/exactmatch/ru/common/v9/search. It is aggressively rate-limited (HTTP 429), so all requests share a session, keep a minimum interval, and retry with exponential backoff.

The older endpoints most public WB scrapers use (card.wb.ru/cards/v1, feedbacks-by-article) are dead as of 2026; this uses the current ones.

Setup

git clone https://github.com/shndo1337/wildberries-mcp.git
cd wildberries-mcp

python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # Linux/Mac

pip install -r requirements.txt
python server.py             # runs an MCP server over stdio

Use it with Claude / any MCP client

Add to your MCP client config (e.g. Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "wildberries": {
      "command": "python",
      "args": ["C:/path/to/wildberries-mcp/server.py"]
    }
  }
}

Then ask the agent things like "Find wireless earbuds under 2000₽ with rating above 4.5 and compare the top 3 by reviews and price trend."

Example (get_product)

{
  "article": 762015089,
  "name": "Наушники беспроводные A.Pods PRO 2 для iPhone и Android",
  "brand": "world of sound",
  "category": "Наушники беспроводные",
  "current_price_rub": 945.97,
  "rating": "4.6",
  "review_count": 125270
}

Limitations

• Relies on Wildberries' internal endpoints — a change on their side can break tools; the code is structured so each source is isolated and easy to fix.
• Search is rate-limited by WB; heavy use needs the built-in backoff (already included) or proxies.
• No authentication / seller API — this is read-only public product data.