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Cricket Analytics MCP

by manasranjanmeher99
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🏏 板球分析 MCP

一个基于 Model Context Protocol (MCP)、OpenAI、Python 和 Streamlit 构建的 AI 驱动板球分析代理。

该项目允许用户通过 AI 驱动的界面探索板球统计数据，并就球员、球队、比赛和球员比较提出自然语言问题。

✨ 功能特性

  • 🏏 板球球员统计

  • 📊 球员表现分析

  • ⚔️ 比较两名板球球员

  • 🏆 球队统计与分析

  • 📅 比赛结果和近期比赛

  • 📰 板球新闻搜索

  • 🤖 由 OpenAI 驱动的板球分析师

  • 🔌 用于公开板球工具的 MCP 服务器

  • 🎨 交互式 Streamlit Web 界面

  • 🔐 基于环境变量的 API 密钥配置

Related MCP server: cricket-mcp

🏗️ 架构

                    ┌─────────────────────┐
                    │    Streamlit UI     │
                    │  streamlit_app.py   │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │    OpenAI Agent      │
                    │   agent_openai.py    │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │     MCP Server      │
                    │      server.py       │
                    └──────────┬──────────┘
                               │
                               ▼
                    ┌─────────────────────┐
                    │    Data Provider    │
                    │  data_provider.py   │
                    └─────────────────────┘

📁 项目结构

cricket-analytics-mcp/
│
├── agent_openai.py       # OpenAI-powered cricket agent
├── data_provider.py      # Cricket data and statistics provider
├── server.py             # MCP server and cricket tools
├── streamlit_app.py      # Streamlit frontend
│
├── screenshots/          # Application screenshots
│   ├── player-stats.png
│   ├── player-comparison.png
│   └── dashboard.png
│
├── .env                  # API keys and configuration (not committed)
├── .gitignore            # Git ignored files
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

🛠️ 技术栈

  • Python

  • OpenAI API

  • Model Context Protocol (MCP)

  • Streamlit

  • python-dotenv

  • 板球统计数据/数据源

🔧 MCP 工具

MCP 服务器可以公开诸如以下的板球工具：

get_player_stats()
get_team_stats()
get_match_results()
get_player_comparison()
get_recent_matches()
search_cricket_news()

这些工具允许 AI 代理检索结构化的板球信息，并使用这些信息来回答用户的问题。

💬 示例查询

Show me Virat Kohli's ODI statistics.

Compare Virat Kohli and Rohit Sharma in ODI cricket.

Show me the recent matches of India.

Give me the Test statistics of Virat Kohli.

Compare the performance of two cricket players.

Show recent cricket news.

⚙️ 安装

1. 克隆仓库

git clone https://github.com/YOUR_USERNAME/cricket-analytics-mcp.git
cd cricket-analytics-mcp

2. 创建虚拟环境

Windows：

python -m venv .venv
.venv\Scripts\activate

3. 安装依赖

pip install -r requirements.txt

4. 配置环境变量

在项目根目录创建一个 .env 文件：

OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=gpt-5.6
CRICAPI_KEY= xxxxxxx

切勿将你的 .env 文件提交到 GitHub。

▶️ 运行应用程序

启动 Streamlit 应用程序

streamlit run streamlit_app.py

然后打开终端中显示的本地 Streamlit URL。

启动 MCP 服务器

如果你的项目配置为单独运行 MCP 服务器：

python server.py

请遵循你的客户端/代理设置所使用的 MCP 配置。

🧪 项目工作流程

User Question
      ↓
Streamlit Interface
      ↓
OpenAI Agent
      ↓
MCP Tool Selection
      ↓
MCP Server
      ↓
Cricket Data Provider
      ↓
Structured Cricket Data
      ↓
OpenAI Analysis
      ↓
Streamlit Response

🔐 环境变量

变量 描述

OPENAI_API_KEY OpenAI API 密钥 OPENAI_MODEL 代理使用的 OpenAI 模型 CRICAPI_KEY Cric API 密钥

示例：

OPENAI_API_KEY=sk-xxxxxxxx
OPENAI_MODEL=gpt-5.6
CRICAPI_KEY= xxxxxxx

📸 截图

将应用程序的截图添加到 screenshots/ 目录中。

推荐的截图：

  • 球员统计

  • 球员比较

  • 板球分析仪表板

然后在 README 中引用它们：

![Player Statistics](screenshots/player-stats.png)

![Player Comparison](screenshots/player-comparison.png)

🚀 未来改进

  • 实时板球比分

  • 实时比赛解说

  • 球员排名

  • 高级球员比较图表

  • 球队表现分析

  • 历史比赛分析

  • 板球新闻聚合

  • 更多 MCP 工具

  • 改进的仪表板可视化

  • 部署到 Streamlit Community Cloud

🎯 使用场景

此项目可用于：

  • 板球统计探索

  • 球员表现分析

  • AI 驱动的板球研究

  • 板球数据可视化

  • MCP 学习与实验

  • 智能体 AI 作品集开发

👨💻 作者

Manas Ranjan Meher

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