zai-vision

name	zai-vision
description	Dynamic access to zai-vision MCP server (8 tools, transport: stdio)

zai-vision Skill

This skill provides dynamic access to the zai-vision MCP server with progressive disclosure loading.

Transport Protocol

Protocol: Standard Input/Output (stdio)

Context Efficiency

Traditional MCP approach:

• All 8 tools loaded at startup
• Estimated context: 4000 tokens

This skill approach:

• Metadata only: ~150 tokens
• Full instructions (when used): ~5k tokens
• Tool execution: 0 tokens (runs externally)

Available Tools

ui_to_artifact - Convert UI screenshots into various artifacts: code, prompts, design specifications, or descriptions. extract_text_from_screenshot - Extract and recognize text from screenshots using advanced OCR capabilities. diagnose_error_screenshot - Diagnose and analyze error messages, stack traces, and exception screenshots. understand_technical_diagram - Analyze and explain technical diagrams including architecture diagrams, flowcharts, UML, ER diagrams, and system design diagrams. analyze_data_visualization - Analyze data visualizations, charts, graphs, and dashboards to extract insights and trends. ui_diff_check - Compare two UI screenshots to identify visual differences and implementation discrepancies. analyze_image - General-purpose image analysis for scenarios not covered by specialized tools. analyze_video - Analyze video content using advanced AI vision models.

Usage Pattern

When the user's request matches this skill's capabilities:

Step 1: Identify the right tool from the list above

Step 2: Generate a tool call in this JSON format:

{
  "tool": "tool_name",
  "arguments": {
    "param1": "value1",
    "param2": "value2"
  }
}

Step 3: Execute via bash:

cd $SKILL_DIR
python3 executor.py --call 'YOUR_JSON_HERE'

⚠️ 重要: Replace $SKILL_DIR with the actual discovered path of this skill directory.

Getting Tool Details

If you need detailed information about a specific tool's parameters:

cd $SKILL_DIR
python3 executor.py --describe tool_name

Examples

Example 1: List all tools

cd $SKILL_DIR
python3 executor.py --list

Example 2: Describe a tool

cd $SKILL_DIR
python3 executor.py --describe tool_name

Example 3: Call a tool

cd $SKILL_DIR
python3 executor.py --call '{"tool": "tool_name", "arguments": {"param1": "value"}}'

Example 4: Call a tool with parameters

cd $SKILL_DIR
python3 executor.py --call '{
  "tool": "ui_to_artifact",
  "arguments": {
    "image_source": "/path/to/image.png",
    "output_type": "code",
    "prompt": "Generate React code"
  }
}'

Error Handling

If the executor returns an error:

• Check the tool name is correct
• Verify required arguments are provided
• Ensure the MCP server is accessible
• Check API keys in mcp-config.json

Performance Notes

Context usage comparison:

Scenario	MCP (preload)	Skill (dynamic)
Idle	4000 tokens	150 tokens
Active	4000 tokens	5k tokens
Executing	4000 tokens	0 tokens

Savings: ~96% reduction in typical usage

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This skill was auto-generated from MCP server configuration Generator: mcp-to-skill (simplified)