mseep-kaltura-mcp 0.1.0

Kaltura MCP Server for LLM integration

Kaltura Model Context Protocol (MCP) Server

The Kaltura MCP Server is an implementation of the Model Context Protocol (MCP) that provides AI models with access to Kaltura's media management capabilities.

Overview

This server enables AI models to:

• Upload media to Kaltura
• Retrieve media metadata
• Search for media
• Manage categories
• Manage users and permissions

By implementing the Model Context Protocol, this server allows AI models to interact with Kaltura's API in a standardized way, making it easier to integrate Kaltura's capabilities into AI workflows.

Requirements

• Python: 3.10 or higher (3.10, 3.11, 3.12 are officially supported)
• Operating Systems: Linux, macOS, Windows
• Dependencies: See pyproject.toml for a complete list

Repository Structure

The kaltura-mcp-public repository contains the complete, self-contained Kaltura MCP server implementation, including:

• All necessary code
• Comprehensive documentation
• Docker support
• Setup script
• Example clients
• Test scripts

Installation

Using Docker

Option 1: Using Pre-built Docker Image

The easiest way to get started is with our pre-built multi-architecture Docker image (supports both x86_64/amd64 and ARM64/Apple Silicon):

# Pull the latest image
docker pull ghcr.io/zoharbabin/kaltura-mcp:latest

# Create a config file
cp config.yaml.example config.yaml
# Edit config.yaml with your Kaltura API credentials

# Run the container
docker run -p 8000:8000 -v $(pwd)/config.yaml:/app/config.yaml ghcr.io/zoharbabin/kaltura-mcp:latest

Option 2: Building Locally with Docker Compose

Alternatively, you can build the image locally:

# Clone the repository
git clone https://github.com/zoharbabin/kaltura-mcp.git
cd kaltura-mcp

# Build and run with Docker Compose
docker-compose up

Manual Installation

# Clone the repository
git clone https://github.com/zoharbabin/kaltura-mcp.git
cd kaltura-mcp

# Create a virtual environment (Python 3.10 or higher required)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -e .

# Configure the server
cp config.yaml.example config.yaml
# Edit config.yaml with your Kaltura API credentials

# Run the server
python -m kaltura_mcp.server

Configuration

The Kaltura MCP Server supports a unified configuration system that works with both YAML and JSON formats. To get started:

1. Copy config.yaml.example to config.yaml and edit it with your Kaltura API credentials:

kaltura:
  partner_id: YOUR_PARTNER_ID
  admin_secret: YOUR_ADMIN_SECRET
  user_id: YOUR_USER_ID
  service_url: https://www.kaltura.com/api_v3

2. You can also use environment variables for configuration:

export KALTURA_PARTNER_ID=YOUR_PARTNER_ID
export KALTURA_ADMIN_SECRET=YOUR_ADMIN_SECRET
export KALTURA_USER_ID=YOUR_USER_ID

For more detailed configuration options, see the Configuration Guide.

Usage

With Claude

To use the Kaltura MCP Server with Claude, see the Using with Claude guide.

With the MCP CLI

To use the Kaltura MCP Server with the MCP CLI, see the Using with MCP CLI guide.

Programmatically

To use the Kaltura MCP Server programmatically, see the examples directory.

Available Tools

The Kaltura MCP Server provides the following tools:

• media_upload: Upload media files to Kaltura
• media_get: Retrieve media metadata
• media_update: Update media metadata
• media_delete: Delete media
• category_list: List categories
• category_get: Retrieve category metadata
• category_add: Add a new category
• category_update: Update category metadata
• category_delete: Delete a category
• user_list: List users
• user_get: Retrieve user metadata
• user_add: Add a new user
• user_update: Update user metadata
• user_delete: Delete a user

Available Resources

The Kaltura MCP Server provides the following resources:

• media://{entry_id}: Media entry metadata
• category://{category_id}: Category metadata
• user://{user_id}: User metadata

Contributing

See CONTRIBUTING.md for details on how to contribute to this project.

License

This project is licensed under the AGPLv3 License - see the LICENSE file for details.