photographi
About
A local computer vision engine that lets AI agents understand the technical metrics of photographs
Details
- Author
- prasadabhishek
- Categories
- Productivity, Media, AI
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Setup
Install photographi in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/prasadabhishek/photographi-mcp
Follow the installation instructions in the repository README, then restart your MCP client.
Fast, private, and grounded technical photo analysis for AI applications.
photographi-mcpis 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 byphoto-quality-analyzer-core) to evaluate sharpness, focus, and exposure—enabling capabilities like automated culling, burst ranking, and metadata indexing without requiring a cloud upload.
- 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.
- 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 strictlyobjective. It evaluates photos based on technical metrics and computer vision (sharpness, exposure, noise, etc.). It doesnotunderstand artistic intent, aesthetics, or "vibe." A blurry, underexposed photo may be an artistic masterpiece, butphotographiwill correctly flag it as technically poor.
For the science and math behind it, see theTechnical Documentation.
Here are real examples from actual photo analysis:
{ "overallConfidence": 0.89, "judgement": "Excellent", "keyMetrics": { "sharpness": 0.94, "exposure": 0.87, "composition": 0.85 } }
Verdict:Tack sharp on subject, well exposed, strong composition.
{ "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.
photographi-mcpenables AI models to perform deep technical audits through these standardized tools:
claude mcp add --scope user photographi uvx photographi-mcp
Add to~/Library/Application Support/Claude/claude_desktop_config.json:
{ "mcpServers": { "photographi": { "command": "uvx", "args": ["photographi-mcp"] } } }
Add to~/.config/github-copilot/config.json:
{ "mcp_servers": { "photographi": { "command": "uvx", "args": ["photographi-mcp"] } } }
photographiis built on aPrivacy-Firstphilosophy.
- Anonymized Aggregates Only: We never collect filenames, paths, or EXIF data.
- Total Transparency: Audit our collection logic directly inanalytics.py.
- Opt-Out: Set the environment variablePHOTOGRAPHI_TELEMETRY_DISABLED=1or use the--disable-telemetryflag.
- 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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