Vision MCP
# Vision MCP
MCP server for image analysis using Vision Language Models.
## Quickstart
1. Install `uv` (Python package manager):
```sh
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Configure your MCP client (e.g., Claude Desktop):
Go to `Claude > Settings > Developer > Edit Config > claude_desktop_config.json`:
```json
{
"mcpServers": {
"Vision": {
"command": "uvx",
"args": ["vision-mcp"],
"env": {
"OPENAI_API_KEY": "your-api-key",
"OPENAI_API_BASE": "https://api.openai.com",
"OPENAI_MODEL": "gpt-4o"
}
}
}
}
```
## Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | API key for authentication |
| `OPENAI_API_BASE` | Yes | API base URL |
| `OPENAI_MODEL` | Yes | Model name for vision tasks |
## Available Tools
| Tool | Description |
|------|-------------|
| `analyze_image` | Analyze images using Vision Language Model |
### analyze_image
Analyze and understand image content from files or URLs.
**Parameters:**
- `prompt` (str): The text prompt describing what to analyze
- `image_source` (str): Image URL or local file path
**Supported formats:** JPEG, PNG, WebP
## License
MIT
## Acknowledgments
This project is inspired by [MiniMax-Coding-Plan-MCP](https://github.com/MiniMax-AI/MiniMax-Coding-Plan-MCP) by MiniMax AI.
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool 'analyze_image' has a singular, clearly defined purpose of analyzing image content, making disambiguation perfect.
A single tool inherently has consistent naming, as there are no other tools to compare it against. The name 'analyze_image' follows a clear verb_noun pattern, which would be consistent if more tools were added.
A single tool for a 'Vision MCP' server feels thin and incomplete for the apparent scope of image analysis. While the tool is powerful, the domain likely requires more operations (e.g., image processing, comparison, or metadata extraction) to be well-scoped, making the count too low.
The tool surface is severely incomplete for a vision-related domain. It only provides analysis, with no support for other common operations like image generation, editing, filtering, or batch processing. This will cause agent failures when tasks require more than basic analysis.