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MCP Server for NBA Stats Predictor Application

@dhrbtjr0331

代码仓库

基本信息

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其他

传输方式

stdio

作者

dhrbtjr0331

关于 MCP Server for NBA Stats Predictor Application

MCP server of NBA stats predictor app that generates player performance forecasts using real-time data analysis and advanced statistical modeling

MCP Server for NBA Stats Predictor Application 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "nba-stats-predictor-mcp": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

MCP Server for NBA Stats Predictor Application 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Server for NBA Stats Predictor Application?

An MCP-powered tool that generates player performance forecasts for upcoming NBA games using real-time data analysis and advanced statistical modeling. It integrates with Claude Desktop, allowing users to query predictions via natural language.

How to use MCP Server for NBA Stats Predictor Application?

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.

Key features of MCP Server for NBA Stats Predictor Application

  • 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

Use cases of MCP Server for NBA Stats Predictor Application

  • Asking Claude Desktop to predict a player's points, rebounds, or assists for an upcoming game
  • Comparing multiple player forecasts to inform fantasy basketball decisions
  • Getting quick performance insights during game previews or analysis

常见问题

1

What are the prerequisites for using this server?

Python 3.8+, pip, and Claude Desktop are required. A virtual environment is recommended.

2

How do I configure Claude Desktop to use this server?

Add an entry to `claude_desktop_config.json` with the command pointing to the project’s `.venv/bin/uv` and arguments including the project directory and `run mcp_main.py`. Replace the path placeholder with your actual project path.

3

What do I do if the MCP tool isn’t working?

Verify all paths in the configuration are correct, ensure the virtual environment is activated, all dependencies are installed, and the FastAPI server is running before using the MCP tool.

4

How do I run the MCP server after installation?

Open a terminal in the project directory, activate the virtual environment, then run `uv run mcp_main.py`. The server must be running for Claude Desktop to connect.

5

Do I need to train the prediction model myself?

Yes. After downloading the data with `python3 data_pipeline/download_data.py`, you must train the model using `python3 models/train_model.py` before the server can make predictions.

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