Which integrations are available for this server?

Provides tools for image analysis using OpenAI-compatible vision models. Allows AI agents to analyze images, extract text, diagnose screenshots, understand technical diagrams, analyze data visualizations, check UI differences, and convert UI to artifacts via any OpenAI-compatible vision API endpoint.

How do I use z_ai_vision_mcp_server_clone?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@z_ai_vision_mcp_server_clone Diagnose this error screenshot" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

z_ai_vision_mcp_server_clone

by 1orZero

Overview Schema Related Servers Score Discussions

JavaScript

Hybrid

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

Related MCP server: imagine-mcp

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"
      }
    }
  }
}

Install Server

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quality

maintenance

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