nba-nba-stats-predictor-mcp

内容来源：README.md（说明文档） · 原始地址 · 查看安装指南

原始内容

# MCP Server for NBA Stats Predictor Application 

An MCP-powered tool for the NBA stats predictor app that generates player performance forecasts using real-time data analysis and advanced statistical modeling.

[![Demo](https://img.youtube.com/vi/8uVuVz1Fbu0/0.jpg)](https://youtu.be/8uVuVz1Fbu0)
## Installation

### Prerequisites
- Python 3.8+
- pip
- Claude Desktop

### Step-by-Step Setup

1. Clone this repository onto your local device

2. Navigate to the project directory:
   ```bash
   cd nba-stats-predictor-application
   ```

3. Create a virtual environment:
   ```bash
   python3 -m venv venv
   ```

4. Activate the virtual environment:
   ```bash
   source venv/bin/activate
   ```

5. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

6. Download the necessary data:
   ```bash
   python3 data_pipeline/download_data.py
   ```

7. Train the prediction model:
   ```bash
   python3 models/train_model.py
   ```

8. Start the FastAPI server:
   ```bash
   uvicorn api.fastapi_server:app --reload
   ```

9. Open a new terminal

10. Return to the project directory

11. Install UV package manager:
    ```bash
    curl -LsSf https://astral.sh/uv/install.sh | sh
    ```

12. Restart the terminal in this directory

13. Run the MCP server:
    ```bash
    uv run mcp_main.py
    ```

14. Open another new terminal

15. Configure Claude Desktop:
    ```bash
    code ~/Library/Application\ Support/Claude/claude_desktop_config.json
    ```
    Note: If the file doesn't exist, create it.

16. Add the following configuration to `claude_desktop_config.json`:
    ```json
    {
        "mcpServers": {
            "NBA-stats-predictor": {
                "command": "/PATH/TO/PROJECT/DIRECTORY/.venv/bin/uv",
                "args": [
                    "--directory",
                    "/PATH/TO/PROJECT/DIRECTORY/",
                    "run",
                    "mcp_main.py"
                ]
            }
        }
    }
    ```
    Remember to replace `/PATH/TO/PROJECT/DIRECTORY/` with the actual path to your project.

17. You should now be able to use this MCP tool on Claude Desktop.

## Usage

Once configured, you can use the NBA stats predictor tool in Claude Desktop to get predictions for player performance in upcoming games.

## Troubleshooting

- Make sure all paths in the configuration are correct
- Ensure the virtual environment is activated before running commands
- Check that all dependencies are properly installed
- Verify that the FastAPI server is running before using the MCP tool

MCP Server for NBA Stats Predictor Application

An MCP-powered tool for the NBA stats predictor app that generates player performance forecasts using real-time data analysis and advanced statistical modeling.

Installation

Prerequisites

Python 3.8+
pip
Claude Desktop

Step-by-Step Setup

Clone this repository onto your local device
Navigate to the project directory:
```
cd nba-stats-predictor-application
```
Create a virtual environment:
```
python3 -m venv venv
```
Activate the virtual environment:
```
source venv/bin/activate
```
Install dependencies:
```
pip install -r requirements.txt
```
Download the necessary data:
```
python3 data_pipeline/download_data.py
```
Train the prediction model:
```
python3 models/train_model.py
```

Start the FastAPI server:

uvicorn api.fastapi_server:app --reload

Open a new terminal
Return to the project directory

Install UV package manager:

curl -LsSf https://astral.sh/uv/install.sh | sh

Restart the terminal in this directory
Run the MCP server:
```
uv run mcp_main.py
```
Open another new terminal

Configure Claude Desktop:

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

Note: If the file doesn't exist, create it.

Add the following configuration to claude_desktop_config.json:

{
    "mcpServers": {
        "NBA-stats-predictor": {
            "command": "/PATH/TO/PROJECT/DIRECTORY/.venv/bin/uv",
            "args": [
                "--directory",
                "/PATH/TO/PROJECT/DIRECTORY/",
                "run",
                "mcp_main.py"
            ]
        }
    }
}

Remember to replace /PATH/TO/PROJECT/DIRECTORY/ with the actual path to your project.

You should now be able to use this MCP tool on Claude Desktop.

Usage

Once configured, you can use the NBA stats predictor tool in Claude Desktop to get predictions for player performance in upcoming games.

Troubleshooting

Make sure all paths in the configuration are correct
Ensure the virtual environment is activated before running commands
Check that all dependencies are properly installed
Verify that the FastAPI server is running before using the MCP tool