MCP Server for NBA Stats Predictor Application
About
MCP server of NBA stats predictor app that generates player performance forecasts using real-time data analysis and advanced statistical modeling
Details
- Author
- dhrbtjr0331
- GitHub stars
- 2
- Downloads
- 132
- Categories
- Other
Jump to
- Leverages real-time data analysis and statistical modeling
- Provides player performance forecasts for upcoming games
- Integrates with Claude Desktop via MCP protocol
- Requires local data download and model training
- Uses FastAPI as the backend server
- Handles all setup steps from a single repository
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP Server for NBA Stats Predictor ApplicationCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, install Python dependencies, download data, train the prediction model, start the FastAPI server, then run the MCP server with uv run mcp_main.py. Configure Claude Desktop by adding the server to claude_desktop_config.json with the correct project path. Once configured, ask Claude for player performance predictions.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp server for nba stats predictor application": {
"nba-stats-predictor-mcp": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"nba-stats-predictor-mcp": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}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 DesktopStep-by-Step Setup
1. Clone this repository onto your local device
2. Navigate to the project directory:
cd nba-stats-predictor-application
3. Create a virtual environment:
python3 -m venv venv
4. Activate the virtual environment:
source venv/bin/activate
5. Install dependencies:
pip install -r requirements.txt
6. Download the necessary data:
python3 data_pipeline/download_data.py
7. Train the prediction model:
python3 models/train_model.py
8. Start the FastAPI server:
uvicorn api.fastapi_server:app --reload
9. Open a new terminal
10. Return to the project directory
11. Install UV package manager:
curl -LsSf https://astral.sh/uv/install.sh | sh
12. Restart the terminal in this directory
13. Run the MCP server:
uv run mcp_main.py
14. Open another new terminal
15. Configure Claude Desktop:
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:
{
"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
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