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Screenshot Analyzer MCP

AI-powered screenshot analysis for UI/UX issues using GPT-5.2

A Model Context Protocol (MCP) server that acts as "eyes" for AI coding assistants, analyzing app screenshots to identify UI/UX problems, compare designs with implementations, and provide actionable fixes.

Features

  • Single/Multi Screenshot Analysis - Analyze 1-10 screenshots at once

  • Batch Analysis - Process entire folders with automatic batching

  • Design Comparison - Compare design mockups vs actual screenshots

  • Fast Mode - Quick scans with gpt-4o-mini at lower cost

  • Report History - Store and retrieve past analysis reports

  • Concurrent Loading - Parallel URL fetching (10x faster)

  • Auto Retry - Exponential backoff for rate limits

Requirements

  • Python 3.10+

  • OpenAI API key (with GPT-5.2 or GPT-4o access)

  • Claude Code CLI (for MCP integration)

Installation

1. Clone the Repository

git clone https://github.com/YOUR_USERNAME/screenshot-mcp.git
cd screenshot-mcp

2. Install Dependencies

pip install mcp httpx pillow

Or with requirements.txt:

pip install -r requirements.txt

3. Add to Claude Code

Add to your global settings (~/.claude/settings.json):

{
  "mcpServers": {
    "screenshot-analyzer": {
      "command": "python3",
      "args": ["/path/to/screenshot-mcp/server.py"]
    }
  }
}

Or use Claude CLI:

claude mcp add screenshot-analyzer python3 /path/to/screenshot-mcp/server.py

4. Set Your API Key

In Claude Code, run:

Use set_api_key tool with your OpenAI API key

Or create config.json in the same directory:

{
  "api_key": "sk-proj-your-api-key-here",
  "default_reasoning_effort": "high"
}

Available Tools

Tool

Description

analyze_screenshots

Analyze screenshots for UI/UX issues

batch_analyze

Batch analyze folder or multiple files

compare_designs

Compare design mockup vs implementation

set_fast_mode

Toggle fast/precise mode

get_report_history

List recent analysis reports

get_report_by_id

Retrieve a specific report

get_last_report

Get the most recent report

set_api_key

Configure OpenAI API key

set_reasoning_effort

Set reasoning level (none/low/medium/high/xhigh)

get_config

View current configuration

Usage Examples

Basic Analysis

analyze_screenshots(
    platform="ios",
    image_paths=["/path/to/screenshot.png"],
    app_description="E-commerce app for fashion retail"
)

Batch Analysis

batch_analyze(
    platform="android",
    folder_path="/path/to/screenshots",
    batch_size=5,
    generate_summary=True
)

Design Comparison

compare_designs(
    design_path="/path/to/figma-export.png",
    screenshot_path="/path/to/actual-screenshot.png",
    platform="ios",
    tolerance="normal"  # strict | normal | relaxed
)

Fast Mode (Lower Cost)

# Enable fast mode (uses gpt-4o-mini)
set_fast_mode(enabled=True)

# Run analysis at ~80% lower cost
analyze_screenshots(platform="web", image_paths=[...])

# Disable for detailed analysis
set_fast_mode(enabled=False)

Output Format

Analysis Report

## Summary
[X critical, Y major, Z minor issues found]

## Critical Issues (Must Fix)
- [Location]: [Problem] → [Fix]

## Major Issues
- [Location]: [Problem] → [Fix]

## Minor Issues
- [Location]: [Problem] → [Fix]

## Suggestions
- [Enhancement idea]

Comparison Report

## Consistency Score: XX/100

## Critical Differences (Design Intent Broken)
- [Element]: Design [value] → Actual [value]

## Major Differences (Noticeable)
- [Element]: Design [value] → Actual [value]

## Minor Differences (Acceptable)
- [Element]: Design [value] → Actual [value]

Configuration Options

Option

Default

Description

api_key

(required)

OpenAI API key

default_model

gpt-5.2

Model for analysis

default_reasoning_effort

high

Reasoning level

max_image_size

1024

Max dimension in pixels

jpeg_quality

85

Compression quality

fast_mode

false

Use faster/cheaper model

fast_mode_model

gpt-4o-mini

Model for fast mode

fast_mode_reasoning

low

Reasoning in fast mode

Tolerance Levels (Design Comparison)

Level

Description

strict

Flag all differences, even 1px variations

normal

Flag noticeable differences (2-4px, visible color mismatches)

relaxed

Only flag significant differences affecting UX

Technical Details

  • Uses OpenAI GPT-5.2 Responses API (/v1/responses)

  • Automatic image compression (max 1024px, JPEG 85%)

  • Concurrent URL loading with asyncio.gather()

  • Exponential backoff retry (429/5xx errors)

  • Report history cache (last 20 reports)

  • Supports PNG, JPEG, GIF, WebP, BMP formats

  • Auto-repair corrupted images (e.g., Android screencap with text header)

Platform Support

Platform

Guidelines

ios

Human Interface Guidelines (HIG)

android

Material Design

web

WCAG, modern web standards

desktop

Platform-specific guidelines

Troubleshooting

"API key not configured"

Run set_api_key tool or create config.json with your key.

"Request timed out"

  • Reduce number of images per request

  • Enable fast mode for quicker responses

  • Use batch_analyze for many images

"429 Rate Limit"

The server automatically retries with exponential backoff. If persistent, wait a few minutes or upgrade your OpenAI plan.

Corrupted Images (Android screencap)

If your screenshots have text/warnings embedded at the beginning (common with Android adb exec-out screencap), the MCP will automatically repair them by finding the actual image data.

To prevent this in your capture scripts:

# Redirect stderr to suppress warnings
adb exec-out screencap -p 2>/dev/null > screenshot.png

# Or specify display ID explicitly
adb exec-out screencap -d 0 -p > screenshot.png

License

MIT License - see LICENSE file for details.

Contributing

Pull requests welcome! Please ensure:

  1. Code follows existing style

  2. Add tests for new features

  3. Update README for new tools


Made with Claude Code

-
security - not tested
A
license - permissive license
-
quality - not tested

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