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Cricket Analytics MCP

by manasranjanmeher99
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Python
Remote

🏏 Cricket Analytics MCP

An AI-powered Cricket Analytics Agent built with Model Context Protocol (MCP), OpenAI, Python, and Streamlit.

The project allows users to explore cricket statistics and ask natural-language questions about players, teams, matches, and player comparisons through an AI-powered interface.

✨ Features

  • 🏏 Cricket player statistics

  • 📊 Player performance analysis

  • ⚔️ Compare two cricket players

  • 🏆 Team statistics and analysis

  • 📅 Match results and recent matches

  • 📰 Cricket news search

  • 🤖 OpenAI-powered cricket analyst

  • 🔌 MCP server for exposing cricket tools

  • 🎨 Interactive Streamlit web interface

  • 🔐 Environment-based API key configuration

Related MCP server: cricket-mcp

🏗️ Architecture

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

📁 Project Structure

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

🛠️ Tech Stack

  • Python

  • OpenAI API

  • Model Context Protocol (MCP)

  • Streamlit

  • python-dotenv

  • Cricket statistics/data sources

🔧 MCP Tools

The MCP server can expose cricket-focused tools such as:

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

These tools allow the AI agent to retrieve structured cricket information and use it to answer user questions.

💬 Example Queries

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.

⚙️ Installation

1. Clone the repository

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

2. Create a virtual environment

Windows:

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

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the project root:

OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=gpt-5.6
CRICAPI_KEY= xxxxxxx

Never commit your .env file to GitHub.

▶️ Running the Application

Start the Streamlit application

streamlit run streamlit_app.py

Then open the local Streamlit URL shown in your terminal.

Start the MCP server

If your project is configured to run the MCP server separately:

python server.py

Follow the MCP configuration used by your client/agent setup.

🧪 Project Workflow

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

🔐 Environment Variables

Variable Description

OPENAI_API_KEY OpenAI API key OPENAI_MODEL OpenAI model used by the agent CRICAPI_KEY Cric API key

Example:

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

📸 Screenshots

Add screenshots of the application to the screenshots/ directory.

Recommended screenshots:

  • Player statistics

  • Player comparison

  • Cricket analytics dashboard

Then reference them in this README:

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

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

🚀 Future Improvements

  • Live cricket scores

  • Live match commentary

  • Player rankings

  • Advanced player comparison charts

  • Team performance analytics

  • Historical match analysis

  • Cricket news aggregation

  • More MCP tools

  • Improved dashboard visualizations

  • Deployment to Streamlit Community Cloud

🎯 Use Cases

This project can be used for:

  • Cricket statistics exploration

  • Player performance analysis

  • AI-powered cricket research

  • Cricket data visualization

  • MCP learning and experimentation

  • Agentic AI portfolio development

👨‍💻 Author

Manas Ranjan Meher

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