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Entertainment MCP Server

MCP Server generated by mcp.ag2.ai

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "ag2-mcp-servers-entertainment-express-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "A JSON string containing the configuration",
        "SECURITY": "Environment variables for security parameters (e.g., API keys)",
        "CONFIG_PATH": "Path to a JSON configuration file (e.g., mcp_server/mcp_config.json)"
      }
    }
  }
}

You are running an MCP (Model Context Protocol) Server that exposes the entertainment API’s OpenAPI-based endpoints to MCP clients. This server lets clients discover, fetch, and interact with the API’s data and capabilities in a structured, protocol-driven way, enabling consistent integration with other MCP-enabled tools and services.

How to use

Start the MCP server in stdio mode to run locally and connect with an MCP client. Once running, the client can request the API’s context, fetch data, and trigger actions supported by the MCP surface. Use the provided runtime command to launch the server, then configure your client to point to the local stdio session.

How to install

Prerequisites: ensure you have Python 3.9 or newer, plus pip and uv installed on your system.

Step by step install and run flow if you are cloning the project locally.

1. Clone the repository to your machine.

2. Install development dependencies.

pip install -e ".[dev]"
```
Alternatively, you can use uv to run the install in editable mode.

3. Start the MCP server in stdio mode.

python mcp_server/main.py stdio
```
This launches the MCP server locally and prepares it to communicate with an MCP client via standard input/output.

4. Configure the server via environment variables if needed. You may specify a configuration path or a JSON string for runtime configuration, and security-related values as required.

5. If you prefer to install dependencies or run tests during development, you can use the project’s tooling for linting, static analysis, and tests as part of your workflow.

Configuration and notes

Runtime configuration can be provided through environment variables or a JSON configuration file. The main entrypoint reads CONFIG_PATH for a JSON configuration file, CONFIG for an inline JSON string, and SECURITY for security-related parameters. These options let you customize endpoints, data access controls, and behavior without changing code.

Building and publishing steps are handled with a modern Python packaging workflow. Use the standard build and publish tooling as needed for your release process.

Security and access control are managed through the configured security parameters. Ensure you provide appropriate keys or tokens in the SECURITY settings to control access to the MCP surface.

Available tools

lint

Run linting to enforce code quality and style rules.

format

Format code to match project style guidelines.

static-analysis

Perform static analysis, including type checks and security scanning.

tests

Execute test suite to verify functionality and reliability.

pre-commit

Run pre-commit hooks to catch issues before commits.

run-server

Start the MCP server in the specified transport mode (stdio in this guide).