NHL v3 Scores

MCP Server generated by mcp.ag2.ai

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "ag2-mcp-servers-nhl-v3-scores": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{...} (JSON string with configuration)",
        "SECURITY": "YOUR_API_KEY",
        "CONFIG_PATH": "mcp_server/mcp_config.json"
      }
    }
  }
}

You run an MCP (Model Context Protocol) server to expose your data source to clients in a standardized way. This server handles the NHL v3 Scores data feed from sportsdata.io and serves it via an MCP-compatible interface, enabling clients to query and participate in model-context interactions without bespoke integration.

How to use

Connect with an MCP client to consume the NHL v3 Scores MCP Server. You will run the local server (stdio transport) and point your client at the process I/O stream. Use the standard Python command shown below to start the server, then configure your client to connect via the provided stdio channel.

python mcp_server/main.py stdio

How to install

Prerequisites you need to satisfy before running the server are Python 3.9 or newer, and the Python package manager with the uv tool available. You also need access to a working network environment to install dependencies and run the server.

Step 1: Clone the server repository to your local machine.

git clone <repository-url>
cd mcp-server

Step 2: Install dependencies. You can install in editable mode with the development extras, or use uv to run the editable install.

pip install -e ".[dev]"
# or, using uv
uv pip install --editable ".[dev]"

Additional setup and running notes

The server supports multiple transport modes, including stdio, server-sent events, and streamable HTTP. For local development you typically start the stdio variant with Python as shown in the installation section.

Available tools

ruff

Lints and formats the codebase to ensure style conformance.

mypy

Performs static type checking to catch type errors early.

pytest

Executes tests to verify functionality and regressions.

hatch

Builds and publishes the project. Also provides development tooling integration.

pre-commit

Runs pre-commit hooks on commits to maintain code quality.