Videoindexer Mcp
What is Videoindexer Mcp
videoindexer-mcp is a Model Context Protocol (MCP) server designed to facilitate interaction with the Azure AI Video Indexer API, providing tools and resources for video analysis and insights.
Use cases
Use cases for videoindexer-mcp include feeding large language models (LLMs) with video insights and automating API interactions for efficient video data processing.
How to use
To use videoindexer-mcp, clone the repository, create and activate a Python virtual environment, install the required dependencies, and run the main script with the appropriate command and arguments as specified in the configuration.
Key features
Key features of videoindexer-mcp include generating prompt content from video insights and retrieving generated prompt content for specific videos.
Where to use
videoindexer-mcp can be used in various fields such as media production, content creation, and any domain that requires video analysis and insights for enhanced decision-making.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Videoindexer Mcp
videoindexer-mcp is a Model Context Protocol (MCP) server designed to facilitate interaction with the Azure AI Video Indexer API, providing tools and resources for video analysis and insights.
Use cases
Use cases for videoindexer-mcp include feeding large language models (LLMs) with video insights and automating API interactions for efficient video data processing.
How to use
To use videoindexer-mcp, clone the repository, create and activate a Python virtual environment, install the required dependencies, and run the main script with the appropriate command and arguments as specified in the configuration.
Key features
Key features of videoindexer-mcp include generating prompt content from video insights and retrieving generated prompt content for specific videos.
Where to use
videoindexer-mcp can be used in various fields such as media production, content creation, and any domain that requires video analysis and insights for enhanced decision-making.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
Video Indexer MCP Server
A Model Context Protocol (MCP) server that provides tools and resources for interacting with Video Indexer APIs.
Features
vi_prompt_content: Generate prompt content from video insightsvi_get_prompt_content: Get the generated prompt content for a video
Use Cases
- feed LLMs with video insights
- Automated API interactions
example config
Replace /path/to/videoindexer-mcp with the actual path to your videoindexer-mcp directory.
Installation
- Clone the repository
- Create and activate a Python virtual environment:
# Create virtual environment
python -m venv mcp-env
# Activate virtual environment
# On Windows:
mcp-env\Scripts\activate
# On Unix or MacOS:
source mcp-env/bin/activate
- Install dependencies:
pip install -r requirements.txt
- Install the package:
pip install -e .
- To deactivate the virtual environment when you’re done:
deactivate
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
