z_ai_vision_mcp_server_clone

OpenAI-compatible MCP server for running image analysis tools against your own vision model endpoint.

Tools

• ui_to_artifact
• extract_text_from_screenshot
• diagnose_error_screenshot
• understand_technical_diagram
• analyze_data_visualization
• ui_diff_check
• analyze_image

Configuration

Set either VISION_ENDPOINT or VISION_BASE_URL.

Variable	Required	Description
VISION_ENDPOINT	Yes, unless VISION_BASE_URL is set	Full chat completions endpoint.
VISION_BASE_URL	Yes, unless VISION_ENDPOINT is set	Base URL; /chat/completions is appended.
VISION_MODEL	Yes	Vision model name sent in the request body.
VISION_API_KEY	No	Bearer token. Omit for local endpoints that do not require auth.
VISION_PROVIDER	No	Label for your provider. Defaults to custom.
VISION_MAX_IMAGE_MB	No	Local image size limit. Defaults to 5.
VISION_TIMEOUT_MS	No	Request timeout. Defaults to 300000.
VISION_TEMPERATURE	No	Optional model temperature.
VISION_TOP_P	No	Optional model top_p.
VISION_MAX_TOKENS	No	Optional max_tokens.

You can also place these values in a local .env file in the working directory where the server starts. Real environment variables override .env values.

Run

npm install
npm run build
VISION_ENDPOINT=http://localhost:11434/v1/chat/completions VISION_MODEL=llava npm start

Or with .env:

npm start

MCP Client Example

{
  "mcpServers": {
    "z-ai-vision-clone": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "z_ai_vision_mcp_server_clone"],
      "env": {
        "VISION_ENDPOINT": "https://your-provider.com/v1/chat/completions",
        "VISION_MODEL": "your-vision-model",
        "VISION_API_KEY": "your-api-key"
      }
    }
  }
}