WildberriesToolsMCP

MCP server for retrieving Wildberries product reviews, designed for seamless integration with LLM clients like Cherry Studio, Claude Desktop, and Cursor.

<details> <summary>👀 Usage example</summary>

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Features

• ✨ Easy Integration: Works out-of-the-box with standard MCP clients.
• 🚀 Smart Scraping: Automatically determines the correct "basket" host for any Wildberries SKU.
• 🔧 LLM Ready: Formats reviews as JSON for easy analysis by Large Language Models.
• 📊 Rich Data: Provides product name, total review count, and raw review texts (up to 500).

Quick Start

# Install dependencies
pip install -r requirements.txt

# Start the MCP server in development mode
mcp dev server.py

Installation

Prerequisites

• Python 3.10 or higher
• pip package manager

Development Setup

1. Clone the repository

   git clone https://github.com/Start-Python-w/wb_smart_remaster.git
   cd wb_smart_remaster
   

2. Create a virtual environment

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows use: .venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

Docker

You can also run the MCP server using Docker. This is useful for keeping your environment clean or for deployment.

Build and Run

1. Build the image

   docker build -t wb-mcp-server .
   

2. Run the container

   docker run -i --rm wb-mcp-server
   

   The -i flag is crucial for MCP to work over Stdio.

Docker Composition

You can use docker-compose to run the server:

docker-compose up

Configuration

Configure your LLM client to use the MCP server.

Client Config (Docker)

If you prefer using the Docker container directly in your client:

• Command: docker
• Args: run, -i, --rm, wb-mcp-server

Cherry Studio 🍒

1. Go to Settings → MCP Servers.
2. Click Add.
3. Configure as Stdio:
   • Name: WB Analyzer
   • Command: /absolute/path/to/project/.venv/bin/python
   • Args: /absolute/path/to/project/server.py
4. Click Save.

Claude Desktop

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "wb-analyzer": {
      "command": "/absolute/path/to/project/.venv/bin/mcp",
      "args": ["run", "server.py"]
    }
  }
}

Note: Replace /absolute/path/to/project/ with the actual path to your project directory.

Usage

Once connected, you can ask your LLM to analyze products directly in the chat.

Example Prompts:

• "Analyze the reviews for this product: [SKU or URL]"
• "What are the pros and cons of item 12345678?"
• "Summarize the customer feedback for this link: https://www.wildberries.ru/catalog/..."

The client will automatically call the get_wb_reviews tool and use the returned data to answer your request.

Tools

Tool	Description
get_wb_reviews	Retrieves product reviews from Wildberries by URL or SKU. Returns JSON with reviews and product info.

License

MIT