Zoom

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": {
    "heesookiim-zoom-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "JSON string containing the configuration",
        "SECURITY": "Environment variables for security parameters (e.g., API keys)",
        "CONFIG_PATH": "Path to JSON configuration file (e.g., mcp_server/mcp_config.json)"
      }
    }
  }
}

You run an MCP (Model Context Protocol) Server to expose a Zoom API contract as a programmable service. It lets you connect an MCP client to the Zoom API specification, enabling tooling, testing, and automation that rely on a structured, protocol-driven interface. This guide shows you how to install, configure, and start the server so you can begin building with MCP clients right away.

How to use

You will start the MCP server in stdio mode and connect an MCP client to it. The server reads its configuration from environment variables you set or from a JSON config file. Once running, your MCP client can request data or actions defined by the Zoom API contract and receive responses in the MCP format. Use the standard client lifecycle to initialize a session, issue requests, and gracefully shut down when you’re done.

How to install

Prerequisites are Python 3.9 or later, plus the Python package manager. You will also use uv as an optional runner.

# Prerequisites
python3 --version
pip --version
uv --version 2>/dev/null || echo "uv not installed; you can install it as shown below"

Step by step to set up and run the MCP server locally.

# 1. Clone the project
git clone <repository-url>
cd mcp_server

# 2. Install dependencies
pip install -e ".[dev]"
# If you prefer using uv to run in editable mode
uv pip install --editable ".[dev]"

Start the server in stdio mode using the primary Python runner. You may optionally configure environment variables for configuration.

python mcp_server/main.py stdio

Configure the server using environment variables or a JSON configuration file. The following environment variables are used to supply configuration details.

CONFIG_PATH=/path/to/mcp_config.json
CONFIG='{"version":2, "transport":"stdio"}'
SECURITY={"API_KEY":"YOUR_API_KEY"}

Additional setup notes

You can run linting, static analysis, and tests during development to maintain code quality. The project uses ruff for linting and formatting, mypy for type checking, and pytest for tests.

# Lint and format
ruff check
ruff format

# Static analysis
./scripts/static-analysis.sh

# Tests with coverage
./scripts/test.sh
./scripts/test-cov.sh

Configuration and security notes

Configure security and access to the MCP server by supplying API keys or other required credentials via environment variables or a configuration file. The example below shows how you can provide a configuration file path and a security key.

CONFIG_PATH=/path/to/mcp_config.json
SECURITY={"API_KEY":"YOUR_API_KEY"}

Building and publishing notes

This project uses a Python packaging workflow for development and distribution. Build and publish commands are available for your workflow.

hatch build
hatch publish

Available tools

linting

ruff checks and formats code to ensure style and quality.

static type checking

mypy analyzes types to prevent type errors.

testing

pytest runs tests; coverage reports can be generated with dedicated scripts.

pre-commit hooks

Enforces code quality before each commit.

build and publish

Hatch builds and publishes the MCP server package.