Integrates with Google's Video Intelligence API to analyze video content, enabling capabilities like object detection, scene detection, explicit content detection, and speech transcription in videos.
MCP Server
This project is an MCP (Multi-Agent Conversation Protocol) Server for the given OpenAPI URL - https://api.apis.guru/v2/specs/googleapis.com/videointelligence/v1p3beta1/openapi.json, auto-generated using AG2's MCP builder.
Prerequisites
- Python 3.9+
- pip and uv
Installation
- Clone the repository:
- Install dependencies:
The .devcontainer/setup.sh script handles installing dependencies using
pip install -e ".[dev]"
. If you are not using the dev container, you can run this command manually.Alternatively, you can useuv
:
Development
This project uses ruff
for linting and formatting, mypy
for static type checking, and pytest
for testing.
Linting and Formatting
To check for linting issues:
To format the code:
These commands are also available via the scripts/lint.sh script.
Static Analysis
To run static analysis (mypy, bandit, semgrep):
This script is also configured as a pre-commit hook in .pre-commit-config.yaml.
Running Tests
To run tests with coverage:
This will run pytest and generate a coverage report. For a combined report and cleanup, you can use:
Pre-commit Hooks
This project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:
The hooks will run automatically before each commit.
Running the Server
The MCP server can be started using the mcp_server/main.py script. It supports different transport modes (e.g., stdio
, sse
, streamable-http
).
To start the server (e.g., in stdio mode):
The server can be configured using environment variables:
CONFIG_PATH
: Path to a JSON configuration file (e.g., mcp_server/mcp_config.json).CONFIG
: A JSON string containing the configuration.SECURITY
: Environment variables for security parameters (e.g., API keys).
Refer to the if __name__ == "__main__":
block in mcp_server/main.py for details on how these are loaded.
The tests/test_mcp_server.py file demonstrates how to start and interact with the server programmatically for testing.
Building and Publishing
This project uses Hatch for building and publishing. To build the project:
To publish the project:
These commands are also available via the scripts/publish.sh script.
This server cannot be installed
This server enables interaction with Google's Video Intelligence API for advanced video analysis, auto-generated using AG2's MCP builder to provide a standardized multi-agent interface.
Related MCP Servers
- -securityFlicense-qualityA server that provides access to Google Gemini AI capabilities including text generation, image analysis, YouTube video analysis, and web search functionality through the MCP protocol.Last updated -2TypeScript
- AsecurityAlicenseAqualityMCP server that exposes Google's Veo2 video generation capabilities, allowing clients to generate videos from text prompts or images.Last updated -77TypeScriptMIT License
- -securityFlicense-qualityAn MCP (Multi-Agent Conversation Protocol) Server that provides a standardized interface for interacting with Google's Cloud Vision API, enabling AI agents to analyze images and extract visual information through natural language.Last updated -Python
- -securityFlicense-qualityThis MCP Server provides a natural language interface to interact with Google's Policy Analyzer API, allowing users to analyze policies and evaluate compliance through conversations.Last updated -Python