vedit-mcp

## Vedit-MCP This is an MCP service for `video editing`, which can achieve basic editing operations with just one sentence. English | [中文](README_CN.md) ## Quick Start ### 1. Install Dependencies #### 1.1 Clone this project or directly download the zip package #### 1.2 Configure the Python environment 1. It is recommended to use uv for installation ```bash cd vedit-mcp uv pip install -r requirements.txt ``` 2. Or install directly using pip ```bash pip install -r requirements.txt ``` #### 1.3 Configure ffmpeg `vedit-mcp.py` relies on `ffmpeg` for implementation. Therefore, please configure ffmpeg. ```bash # For Mac brew install ffmpeg # For Ubuntu sudo apt update sudo apt install ffmpeg ``` ### 2. Start the Service #### 2.1. It is recommended to use `google-adk` to build your own project - Please refer to [adk-sample](sample/adk_sample.py) ##### Before executing this sample script 1. Please ensure that the path format is at least as follows > - sample > - kb > - raw/test.mp4 // This is the original video you need to process > - adk_sample.py > - vedit_mcp.py 2. Please install the following two dependencies ```python # # adk-sample pip install requirements # google-adk==0.3.0 # litellm==1.67.2 ``` 3. Please set the api-key and api-base Currently, this script uses the API of the [`Volcano Ark Platform`](https://www.volcengine.com/product/ark), and you can go there to configure it by yourself. After obtaining the API_KEY, please configure the API_KEY as an environment variable. ```bash export OPENAI_API_KEY="your-api-key" ``` 4. Execute the script ```bash cd sample python adk_sample.py ``` 5. End of execution After this script is executed correctly and ends, a video result file will be generated in kb/result, and a log file will be generated and the result will be output. If you need secondary development, you can choose to add `vedit_mcp.py` to your project for use. #### 2.2 Or build using `cline` Firstly, please ensure that your Python environment and ffmpeg configuration are correct Configure cline_mcp_settings. json as follows ```json { "mcpServers": { "vedit-mcp": { "command": "python", "args": [ "vedit_mcp.py", "--kb_dir", "your-kb-dir-here" ] } } } ``` #### 2.3. Execute using the stramlit web interface To be supplemented ### 3. precautions 1. It is recommended to use the `thinking model` to handle this type of task. Currently, it seems that the `thinking model` performs better in handling this type of task? But no further testing has been conducted, it's just an intuitive feeling.