Hub Marketing

MCP Server generated by mcp.ag2.ai

python

1

GitHub Stars

python

Language

6 months ago

First Indexed

2 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": {
    "chandrahasm-marketing-events-extension": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ]
    }
  }
}

You are running an MCP (Model Context Protocol) Server that serves a precise OpenAPI-defined API surface for Hub APIs marketing endpoints. It enables clients to interact with the underlying data model and actions via a structured MCP interface, making it easier to integrate with code editors, tooling, and automated workflows while keeping implementation details abstracted behind the MCP layer.

How to use

To operate the MCP server, launch it in stdio mode and connect your MCP client to the local process. The server exposes a standardized protocol you can consume from your client, allowing you to query, subscribe to, or invoke actions exposed by the Hub API Marketing surface.

How to install

Prerequisites before installing the MCP server are Python 3.9+ and a working Python environment with pip and uv (uv is used to run editable installs or start the server).

# Step 1: Clone the repository
git clone <repository-url>
cd mcp-server

# Step 2: Install dependencies
pip install -e ".[dev]"

# If you prefer using uv to launch editable installs
uv pip install --editable ".[dev]"

Running the server

Start the MCP server in stdio mode using the main entry point. This runs the server as a local process ready to communicate with an MCP client.

python mcp_server/main.py stdio

Configuration and runtime notes

You can influence runtime behavior through environment variables. Common options include setting a path to a JSON configuration or passing a JSON string directly, as well as security-related parameters. Look for a configuration loading block in the server’s startup routine to see exactly how CONFIG_PATH, CONFIG, and SECURITY are consumed.

Available tools

lint and format

ruff is used for linting and formatting the codebase to ensure consistency and quality.

static analysis

Static analysis with mypy, bandit, and semgrep helps catch type issues and security concerns early.

tests

pytest runs unit tests and generates coverage reports with optional combined reports via dedicated scripts.

pre-commit

Pre-commit hooks automatically run before commits to enforce quality checks.

build and publish

Hatch builds and publishes the package, with scripts available to simplify the process.