Kaltura MCP Server: Streamline Media Workflows with AI

Kaltura's MCP Server lets AI models seamlessly upload, search, and manage media via a standardized protocol, streamlining content workflows effortlessly.

Visit Repository

✨ Developer Tools

4.5(147 reviews)

220 saves

102 comments

70% of users reported increased productivity after just one week

About Kaltura MCP Server

What is Kaltura MCP Server: Streamline Media Workflows with AI?

The Kaltura MCP Server serves as a bridge between AI models and Kaltura's media infrastructure, leveraging the Model Context Protocol (MCP) to enable seamless integration of advanced media management capabilities. This intermediary platform standardizes interactions with Kaltura's API, empowering developers to embed features like automated media ingestion, metadata extraction, and access control into AI-driven workflows without reinventing core functionality.

How to Use Kaltura MCP Server: Streamline Media Workflows with AI?

Deployment Options

Quickstart via pre-built Docker image for multi-architecture support
Local Docker Compose setup for environment reproducibility
Manual installation using Python virtual environments for granular control

Configuration Process

Edit YAML configuration files or utilize environment variables to securely provision Kaltura API credentials. The hybrid configuration system prioritizes security while maintaining operational flexibility.

Integration Paths

Programmatic access through Python SDK examples
Claude integration via documented API patterns
CLI interaction using MCP command-line tools

Kaltura MCP Server Features

Key Features of Kaltura MCP Server: Streamline Media Workflows with AI?

Contextual Media Management

Execute CRUD operations on media entries with full metadata lifecycle management, including granular permission controls through standardized REST endpoints

AI-Ready Abstraction Layer

Pre-built workflows for media ingestion, categorization, and access control reduce development overhead while maintaining compatibility with emerging AI models

Enterprise-Grade Security

Role-based access control (RBAC) framework ensures media assets remain protected through encrypted API interactions and configurable permission hierarchies

Multi-Environment Support

Seamlessly operates across Linux, macOS, and Windows environments with containerized deployment options for consistent behavior

Use Cases of Kaltura MCP Server: Streamline Media Workflows with AI?

Automated Content Curation

AI models can autonomously tag, categorize, and archive media while maintaining audit trails through the server's metadata management interface

Dynamic Access Control

Implement context-aware permissions where ML models determine access rights based on user roles and content sensitivity

Multi-Platform Distribution

Enable AI-driven transcoding and delivery optimizations by abstracting Kaltura's media processing capabilities through standardized API calls

Analytics-Driven Workflows

Facilitate real-time media analysis by exposing Kaltura's metadata infrastructure to predictive analytics models for content recommendation engines

Kaltura MCP Server FAQ

FAQ from Kaltura MCP Server: Streamline Media Workflows with AI?

What distinguishes MCP Server from direct API usage?

Provides abstraction layer for versioning control, reduces boilerplate code, and enforces best practices through standardized interface design

Is zero-downtime deployment supported?

Blue-green deployment strategies are achievable through Docker orchestration, though specific implementation depends on infrastructure setup

How are errors handled?

Extensive logging with structured error responses adhering to MCP protocol specifications, including detailed stack traces for debugging

Can it integrate with legacy systems?

Yes, through REST API compatibility layer and configurable middleware hooks for legacy protocol translation

Content

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:

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

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.