Image Analysis MCP Server

Image Analysis MCP Server

Provides Claude with detailed image inspection capabilities, including metadata extraction, histogram analysis, tonal and color analysis, sharpness detection, and more, supporting both standard and RAW formats.

Category
访问服务器

README

Image Analysis MCP Server

A comprehensive read-only image analysis MCP (Model Context Protocol) server that provides Claude with detailed image inspection capabilities.

Features

  • Metadata & EXIF Extraction: Camera settings, GPS, timestamps, and more
  • Histogram Analysis: RGB and luminance histograms with statistics
  • Tonal Analysis: Shadows, midtones, highlights, dynamic range, and clipping detection
  • Color Analysis: Dominant colors, color temperature, saturation, and color cast detection
  • Spatial Properties: Sharpness scoring, noise estimation, edge density, and blur detection
  • Frequency Analysis: FFT-based detail level assessment (optional)
  • Preview Generation: Base64-encoded preview images (optional)

Supported Formats

Standard Formats

  • JPEG (.jpg, .jpeg)
  • PNG (.png)
  • TIFF (.tif, .tiff)
  • WebP (.webp)

RAW Formats

  • Canon RAW (.cr2, .cr3)
  • Nikon RAW (.nef)
  • Sony RAW (.arw)
  • Adobe DNG (.dng)
  • Fuji RAW (.raf)
  • Olympus RAW (.orf)

Installation

Option 1: Docker (Recommended)

The easiest way to get started - no Python setup required!

# Build the Docker image
cd image_analysis
docker build -t image-analysis-mcp:latest .

# Test it works
docker run --rm image-analysis-mcp:latest python -c "import image_analysis_mcp; print('✓ OK')"

See DOCKER.md for complete Docker documentation.

Option 2: Poetry

# Prerequisites: Python 3.12+, Poetry
cd image_analysis

# Install dependencies
poetry install

# Activate the virtual environment
poetry shell

Option 3: pip

# Prerequisites: Python 3.12+
cd image_analysis

# Install the package
pip install -e .

Usage

Running the MCP Server

The server can be run directly:

# Using Poetry
poetry run image-analysis-mcp

# Or if in activated virtual environment
image-analysis-mcp

Configuration for Claude Desktop

Add to your Claude Desktop configuration file (claude_desktop_config.json):

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

Using Docker (Recommended)

{
  "mcpServers": {
    "image-analysis": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-v",
        "/path/to/your/images:/images:ro",
        "image-analysis-mcp:latest"
      ]
    }
  }
}

Then reference images as /images/photo.jpg in Claude.

Using Poetry

{
  "mcpServers": {
    "image-analysis": {
      "command": "poetry",
      "args": ["run", "image-analysis-mcp"],
      "cwd": "/absolute/path/to/image_analysis"
    }
  }
}

Using pip (Global Install)

{
  "mcpServers": {
    "image-analysis": {
      "command": "image-analysis-mcp"
    }
  }
}

Available Tools

1. get_metadata

Fast metadata and EXIF extraction without loading full image data.

Parameters:

  • filepath (str): Path to image file

Example:

What camera was used for IMG_1234.CR2?

2. get_histogram

Extract RGB and luminance histograms with channel statistics.

Parameters:

  • filepath (str): Path to image file
  • process_raw (bool, optional): Process RAW files (default: True)

Example:

Show me the histogram for landscape.jpg

3. analyze_image

Comprehensive image analysis including all properties.

Parameters:

  • filepath (str): Path to image file
  • include_frequency (bool, optional): Include FFT analysis (default: False)
  • include_preview (bool, optional): Generate preview image (default: False)
  • preview_max_dimension (int, optional): Preview max size (default: 1920)
  • preview_format (str, optional): "JPEG", "PNG", or "WEBP" (default: "JPEG")
  • preview_quality (int, optional): Quality 1-100 (default: 85)
  • process_raw (bool, optional): Process RAW files (default: True)

Example:

Analyze photo.jpg and tell me if it's sharp enough for printing
Analyze this RAW file and show me a preview

Architecture

This server follows a fully stateless architecture:

  • Each tool call is independent
  • Images are loaded, analyzed, and immediately discarded
  • No caching or session state
  • Predictable and reproducible results

Performance

Typical performance on modern hardware:

Operation 2MP Image 12MP Image 50MP Image
get_metadata 10ms 15ms 25ms
get_histogram 50ms 150ms 600ms
analyze_image 200ms 800ms 3000ms
analyze_image (with preview) 300ms 1100ms 4000ms
analyze_image (with FFT) 500ms 2000ms 8000ms

Example Conversations

Check Image Quality

User: Is wedding_photo.jpg sharp enough for a large print?
Claude: [Uses analyze_image to check sharpness, noise, and resolution]

Exposure Analysis

User: Is this sunset photo overexposed?
Claude: [Uses analyze_image to check histogram and clipping]

Color Analysis

User: What are the dominant colors in this landscape?
Claude: [Uses analyze_image to extract dominant colors and temperature]

Metadata Extraction

User: What camera settings were used for IMG_5678.NEF?
Claude: [Uses get_metadata to extract EXIF data]

Error Handling

The server provides clear error messages:

  • FILE_NOT_FOUND: File doesn't exist
  • PERMISSION_DENIED: Cannot read file
  • UNSUPPORTED_FORMAT: Format not supported
  • CORRUPTED_FILE: File is corrupted
  • OUT_OF_MEMORY: Image too large
  • INVALID_PARAMETER: Bad parameter value
  • RAW_PROCESSING_FAILED: RAW conversion failed

Development

Running Tests

poetry run pytest

Code Formatting

poetry run black src/

Type Checking

poetry run mypy src/

License

This project is provided as-is for use with Claude Desktop and MCP.

Author

Jim Dasher

Version

1.0.0 - October 28, 2025

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选