Veo MCP Integration Guide
MCP (Model Context Protocol) is a model context protocol launched by Anthropic that allows AI models (such as Claude, GPT, etc.) to call external tools through a standardized interface. With the Veo MCP Server provided by AceData Cloud, you can directly use Google Veo to generate AI videos within AI clients like Claude Desktop, VS Code, Cursor, and more.
¶ Feature Overview
Veo MCP Server provides the following core features:
- Text-to-Video — Generate high-quality videos from text prompts
- Image-to-Video — Generate videos based on images
- Multi-Model Support — Supports models such as veo3, veo2, veo31-fast-ingredients, etc.
- Multiple Resolutions — Supports output formats including 4K, 1080p, GIF, etc.
- Multiple Aspect Ratios — Supports ratios such as 16:9, 9:16, etc.
- 1080p Upgrade — Upgrade already generated videos to 1080p
- Task Query — Monitor generation progress and retrieve results
¶ Prerequisites
Before use, you need to obtain an AceData Cloud API Token:
- Register or log in to the AceData Cloud Platform
- Go to the Veo Videos API page
- Click "Acquire" to get your API Token (first-time applicants receive free credits)
¶ Installation and Configuration
¶ Method 1: pip Installation (Recommended)
pip install mcp-veo
¶ Method 2: Source Installation
git clone https://github.com/AceDataCloud/VeoMCP.git
cd VeoMCP
pip install -e .
After installation, you can start the service using the mcp-veo command.
¶ Using in Claude Desktop
Edit the Claude Desktop configuration file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Add the following configuration:
{
"mcpServers": {
"veo": {
"command": "mcp-veo",
"env": {
"ACEDATACLOUD_API_TOKEN": "your API Token"
}
}
}
}
If using uvx (no need to pre-install packages):
{
"mcpServers": {
"veo": {
"command": "uvx",
"args": ["mcp-veo"],
"env": {
"ACEDATACLOUD_API_TOKEN": "your API Token"
}
}
}
}
After saving the configuration, restart Claude Desktop to use Veo-related tools in conversations.
¶ Using in VS Code / Cursor
Create .vscode/mcp.json in the project root directory:
{
"servers": {
"veo": {
"command": "mcp-veo",
"env": {
"ACEDATACLOUD_API_TOKEN": "your API Token"
}
}
}
}
Or use uvx:
{
"servers": {
"veo": {
"command": "uvx",
"args": ["mcp-veo"],
"env": {
"ACEDATACLOUD_API_TOKEN": "your API Token"
}
}
}
}
¶ Available Tools
| Tool Name | Description |
|---|---|
veo_text_to_video |
Generate video from text prompts |
veo_image_to_video |
Generate video based on images |
veo_get_1080p |
Upgrade video to 1080p |
veo_get_task |
Query the status of a single task |
veo_get_tasks_batch |
Batch query task statuses |
¶ Usage Examples
After configuration, you can directly invoke these features in AI clients using natural language, for example:
- "Help me generate a starry sky time-lapse video with Veo"
- "Generate a 4K video from this landscape photo"
- "Create a vertical 9:16 short video"
- "Upgrade this video to 1080p"
¶ More Information
- GitHub Repository: AceDataCloud/VeoMCP
- PyPI Package: mcp-veo
- API Documentation: Veo Video Generation API
