MCP ExplorerExplorer

Nba V3 Scores

@ag2-mcp-serverson 5 months ago
1 MIT
FreeCommunity
AI Systems
MCP Server generated by mcp.ag2.ai

Overview

What is Nba V3 Scores

nba-v3-scores is an MCP Server designed to interact with the NBA scores API provided by SportsData.io, enabling users to access real-time basketball scores and statistics.

Use cases

Use cases include developing sports tracking applications, creating dashboards for live score updates, and integrating NBA data into fantasy sports platforms.

How to use

To use nba-v3-scores, clone the repository, install the necessary dependencies, and run the server using the provided scripts. The server can operate in various transport modes such as stdio and sse.

Key features

Key features include automatic generation from OpenAPI specifications, support for linting, static type checking, and testing, as well as pre-commit hooks to maintain code quality.

Where to use

nba-v3-scores can be used in sports applications, analytics platforms, and any software that requires real-time NBA score updates and statistics.

Content

MCP Server

This project is an MCP (Multi-Agent Conversation Protocol) Server for the given OpenAPI URL - https://api.apis.guru/v2/specs/sportsdata.io/nba-v3-scores/1.0/openapi.json, auto-generated using AG2’s MCP builder.

Prerequisites

  • Python 3.9+
  • pip and uv

Installation

  1. Clone the repository:
    git clone <repository-url>
    cd mcp-server
    
  2. Install dependencies:
    The .devcontainer/setup.sh script handles installing dependencies using pip install -e ".[dev]". If you are not using the dev container, you can run this command manually.
    pip install -e ".[dev]"
    
    Alternatively, you can use uv:
    uv pip install --editable ".[dev]"
    

Development

This project uses ruff for linting and formatting, mypy for static type checking, and pytest for testing.

Linting and Formatting

To check for linting issues:

ruff check

To format the code:

ruff format

These commands are also available via the scripts/lint.sh script.

Static Analysis

To run static analysis (mypy, bandit, semgrep):

./scripts/static-analysis.sh

This script is also configured as a pre-commit hook in .pre-commit-config.yaml.

Running Tests

To run tests with coverage:

./scripts/test.sh

This will run pytest and generate a coverage report. For a combined report and cleanup, you can use:

./scripts/test-cov.sh

Pre-commit Hooks

This project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:

pre-commit install

The hooks will run automatically before each commit.

Running the Server

The MCP server can be started using the mcp_server/main.py script. It supports different transport modes (e.g., stdio, sse).

To start the server (e.g., in stdio mode):

python mcp_server/main.py stdio

The server can be configured using environment variables:

  • CONFIG_PATH: Path to a JSON configuration file (e.g., mcp_server/mcp_config.json).
  • CONFIG: A JSON string containing the configuration.
  • SECURITY: Environment variables for security parameters (e.g., API keys).

Refer to the if __name__ == "__main__": block in mcp_server/main.py for details on how these are loaded.

The tests/test_mcp_server.py file demonstrates how to start and interact with the server programmatically for testing.

Building and Publishing

This project uses Hatch for building and publishing.
To build the project:

hatch build

To publish the project:

hatch publish

These commands are also available via the scripts/publish.sh script.

Tools

No tools

Comments

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