Photographi Mcp
Runs computer vision models locally on your photo library to extract technical quality metrics without uploading anything to the cloud. Exposes tools for analyzing sharpness, focus, exposure, and composition on individual files or entire folders. You get operations like analyze_photo for single image audits, rank_photographs to find the sharpest frame in a burst sequence, and cull_photographs to automatically move low quality shots to a subfolder. Also includes color palette extraction and scene content detection across 80+ object categories. Built on top of photo-quality-analyzer-core with gear aware calibration that knows your lens's optimal aperture range. Useful when you need to batch process photo sets, automate culling workflows, or integrate objective quality scoring into LLM driven photography tools.
- GitHub stars
- 5
- tools
- 8
- transport
- STDIO
- last updated
- Feb 16, 2026
Tools
8 toolsPublic tool metadata for what this MCP can expose to an agent.
analyze_photoFull technical audit of sharpness, focus, and lighting.
Full technical audit of sharpness, focus, and lighting.
No parameter schema in public metadata yet.
analyze_folderStatistical summary identifying the best/worst image groups.
Statistical summary identifying the best/worst image groups.
No parameter schema in public metadata yet.
rank_photographsRanks files by technical perfection to find the "hero" frame.
Ranks files by technical perfection to find the "hero" frame.
No parameter schema in public metadata yet.
cull_photographsAutomatically cleans up failed shots into a subfolder.
Automatically cleans up failed shots into a subfolder.
No parameter schema in public metadata yet.
threshold_cullBinary sorting to isolate professional-grade assets.
Binary sorting to isolate professional-grade assets.
No parameter schema in public metadata yet.
get_color_paletteExtracts hexadecimal codes for dominant image aesthetics.
Extracts hexadecimal codes for dominant image aesthetics.
No parameter schema in public metadata yet.
get_folder_palettesBatch color extraction for an entire folder.
Batch color extraction for an entire folder.
No parameter schema in public metadata yet.
get_scene_contentRapid content indexing based on 80+ object categories.
Rapid content indexing based on 80+ object categories.
No parameter schema in public metadata yet.
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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.
👁️ What It Analyzes
Details
- Repository
- github.com/prasadabhishek/photographi-mcp
- Category
- Media & Entertainment
- Package
photographi-mcp- Transport
- STDIO
- Registry
- MCP registry, active
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