Tennis Warehouse MCP Server
English
Overview
Tennis Warehouse MCP Server is a Model Context Protocol (MCP) server that provides LLMs with access to Tennis Warehouse product data, including product search, specifications, and reviews.
Features
- Product Search: Search for tennis products by keyword, category, or brand
- Detailed Specifications: Extract technical specifications from product pages
- Review Data: Get performance ratings and playtester feedback
- Specialized Search: Dedicated search for racquets, bags, and shoes
- Deal Finder: Discover discounted products
- Smart Search: Intelligent search with filtering suggestions
Installation
# Clone the repository
git clone
cd tennis-warehouse-mcp
# Install dependencies
uv syncUsage
Start the MCP Server
# Method 1: Using the launch script
./run_mcp.sh
# Method 2: Direct execution
uv run python main.pyMCP Client Configuration (e.g., CherryStudio)
To configure this server in CherryStudio or other MCP-supported clients, use the following settings based on the client UI:
- Name: Tennis Warehouse (or any custom name)
- Type: stdio (Standard Input / Output)
- Command:
uv(or the absolute path to theuvcommand, which you can find by runningwhich uvin your terminal) - Arguments (enter one argument per line):
--directory
/absolute/path/to/tennis-warehouse-mcp
run
python
/absolute/path/to/tennis-warehouse-mcp/main.py- Env Variables (optional, enter one pair per line):
TW_API_TIMEOUT=10
TW_MAX_RESULTS=20Available Tools
The server provides 11 MCP tools:
search_products- Search tennis productsget_specs- Get product specificationsget_review- Get product reviewssearch_bags- Search tennis bagssearch_racquets- Search tennis racquetssearch_shoes- Search tennis shoesget_categories- Get product categoriescheck_availability- Check product stockget_deals- Find discounted productssmart_search- Intelligent searchhandle_option- Handle search options
Project Structure
tennis-warehouse-mcp/
├── src/ # Source code
│ ├── api/ # API client and parsers
│ ├── tools/ # MCP tool functions
│ └── utils/ # Utilities and constants
├── tests/ # Test scripts
├── docs/ # Documentation
└── main.py # Entry pointDocumentation
- Usage Guide - Detailed usage instructions
- API Documentation - API module reference
- Tools Documentation - MCP tools reference
- Development Guide - Development and contribution guide
Testing
# Run tests
uv run python tests/test_product_review.pyConfiguration
Environment variables:
TW_API_TIMEOUT- API timeout in seconds (default: 10)TW_MAX_RESULTS- Maximum results (default: 20)
Requirements
- Python 3.10+
- uv (recommended) or pip
License
MIT License - see LICENSE file for details.
This project is based on and extends the original Tennis Warehouse MCP implementation.
Chinese
概述
Tennis Warehouse MCP 服务器是一个模型上下文协议(MCP)服务器,为大语言模型提供访问 Tennis Warehouse 产品数据的能力,包括产品搜索、规格查询和评测数据。
功能特性
- 产品搜索:通过关键词、分类或品牌搜索网球产品
- 详细规格:从产品页面提取技术规格参数
- 评测数据:获取性能评分和测试员反馈
- 专业搜索:针对球拍、球包和球鞋的专用搜索
- 优惠查找:发现促销产品
- 智能搜索:带过滤建议的智能搜索
安装
# 克隆仓库
git clone
cd tennis-warehouse-mcp
# 安装依赖
uv sync使用方法
启动 MCP 服务器
# 方式 1:使用启动脚本
./run_mcp.sh
# 方式 2:直接运行
uv run python main.py在 CherryStudio 等客户端中配置
如果你要在 CherryStudio 等支持 MCP 的客户端中配置此服务器,请根据客户端界面使用以下设置:
- 名称: Tennis Warehouse (或任意自定义名称)
- 类型: 标准输入 / 输出 (stdio)
- 命令:
uv(或是系统上uv命令的绝对路径,可通过在终端执行which uv获取) - 参数 (注意:每行填入一个参数):
--directory
/绝对路径/tennis-warehouse-mcp
run
python
/绝对路径/tennis-warehouse-mcp/main.py- 环境变量 (可选,每行填入一个):
TW_API_TIMEOUT=10
TW_MAX_RESULTS=20可用工具
服务器提供 11 个 MCP 工具:
search_products- 搜索网球产品get_specs- 获取产品规格get_review- 获取产品评测search_bags- 搜索网球包search_racquets- 搜索网球拍search_shoes- 搜索网球鞋get_categories- 获取产品分类check_availability- 检查产品库存get_deals- 查找优惠产品smart_search- 智能搜索handle_option- 处理搜索选项
项目结构
tennis-warehouse-mcp/
├── src/ # 源代码
│ ├── api/ # API 客户端和解析器
│ ├── tools/ # MCP 工具函数
│ └── utils/ # 工具和常量
├── tests/ # 测试脚本
├── docs/ # 文档
└── main.py # 入口文件文档
测试
# 运行测试
uv run python tests/test_product_review.py配置
环境变量:
TW_API_TIMEOUT- API 超时时间(秒),默认值:10TW_MAX_RESULTS- 最大结果数,默认值:20
系统要求
- Python 3.10+
- uv(推荐)或 pip
许可证
MIT License - 详见 LICENSE 文件。
本项目基于原始的 Tennis Warehouse MCP 实现进行扩展和改进。
