photographi-mcp

<!-- mcp-name: io.github.prasadabhishek/photographi-mcp --> Fast, private, and grounded technical photo analysis for AI applications.

photographi-mcp is an MCP server that enables AI models and LLM-powered tools to perform technical analysis on local photo libraries. It runs computer vision models directly on your hardware (powered by photo-quality-analyzer-core) to evaluate sharpness, focus, and exposure—enabling capabilities like automated culling, burst ranking, and metadata indexing without requiring a cloud upload.

⚡ Why photographi?

• Technical First: Purpose-built for objective metrics (sharpness, lighting, focus). It provides technical data for evaluating image quality.
• Token Efficient: Save model context by pre-filtering technical metadata locally. Only the most relevant insights are sent to the AI application, keeping sessions fast and lean.
• Privacy First: All analysis happens 100% locally on your machine.
• Low Latency: Built for efficient processing, allowing for rapid ranking and technical feedback on local photo folders.

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👁️ What It Analyzes

• Smart Focus: Detects subjects and verifies they're sharp
• Exposure: Catches blown highlights and blocked shadows
• Gear-Aware: Knows your lens's sweet spot for optimal sharpness
• Composition: Evaluates framing and subject placement
• Quality Alerts: Flags motion blur, diffraction, high ISO noise

[!NOTE] Technical vs. Artistic: This tool is strictly objective. It evaluates photos based on technical metrics and computer vision (sharpness, exposure, noise, etc.). It does not understand artistic intent, aesthetics, or "vibe." A blurry, underexposed photo may be an artistic masterpiece, but photographi will correctly flag it as technically poor.

For the science and math behind it, see the Technical Documentation.

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📸 See It In Action

Here are real examples from actual photo analysis:

Example 1: Excellent Photo

{
  "overallConfidence": 0.89,
  "judgement": "Excellent",
  "keyMetrics": {
    "sharpness": 0.94,
    "exposure": 0.87,
    "composition": 0.85
  }
}

Verdict: Tack sharp on subject, well exposed, strong composition.

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Example 2: Poor Photo

{
  "overallConfidence": 0.20,
  "judgement": "Very Poor",
  "keyMetrics": {
    "sharpness": 0.30,
    "focus": 0.07,
    "exposure": 0.0
  }
}

Verdict: Missed focus on subject, severe underexposure/black clipping, and excessive headroom.

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🛠️ Tools (MCP)

photographi-mcp enables AI models to perform deep technical audits through these standardized tools:

Tool	AI "Intent" Example	Action / Insight Provided
analyze_photo	"Is this dog photo sharp enough for a print?"	Full technical audit of sharpness, focus, and lighting.
analyze_folder	"How's the overall quality of my 'Vacation' folder?"	Statistical summary identifying the best/worst image groups.
rank_photographs	"Find the best shot in this burst of the cake."	Ranks files by technical perfection to find the "hero" frame.
cull_photographs	"Move all the blurry photos to a junk folder."	Automatically cleans up failed shots into a subfolder.
threshold_cull	"Strictly separate keepers using a score of 0.7."	Binary sorting to isolate professional-grade assets.
get_color_palette	"What colors are in this sunset for my website?"	Extracts hexadecimal codes for dominant image aesthetics.
get_folder_palettes	"Generate a moodboard from my 'Forest' shoot."	Batch color extraction for an entire folder.
get_scene_content	"Which photos contain a 'cat' or 'mountain'?"	Rapid content indexing based on 80+ object categories.

Full API Reference

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🚀 Get Started

Claude CLI (Fastest)

claude mcp add --scope user photographi uvx photographi-mcp

Claude Desktop (macOS)

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "photographi": {
      "command": "uvx",
      "args": ["photographi-mcp"]
    }
  }
}

GitHub Copilot CLI

Add to ~/.config/github-copilot/config.json:

{
  "mcp_servers": {
    "photographi": {
      "command": "uvx",
      "args": ["photographi-mcp"]
    }
  }
}

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🔒 Privacy & Telemetry

photographi is built on a Privacy-First philosophy.

• Anonymized Aggregates Only: We never collect filenames, paths, or EXIF data.
• Total Transparency: Audit our collection logic directly in analytics.py.
• Opt-Out: Set the environment variable PHOTOGRAPHI_TELEMETRY_DISABLED=1 or use the --disable-telemetry flag.

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📖 Documentation

• Setup & Config Guide: Detailed configuration and troubleshooting.
• The Science: Math and theory behind the quality scoring.
• Contributing: How to help improve the project.
• GitHub Issues: Report bugs or request features.

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<div align="center"> <p> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a> <a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-Compatible-green.svg" alt="MCP Protocol"></a> <a href="https://glama.ai/mcp/servers/@prasadabhishek/photographi-mcp"><img width="380" height="200" src="https://glama.ai/mcp/servers/@prasadabhishek/photographi-mcp/badge" /></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="Python 3.10+"></a> </p> <p>Built with ❤️ for photographers</p> </div>