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"
      }
    }
  }
}