MCP SERVERScore a folder of photos on sharpness, focus and exposure without uploading any of them — then rank the keepers or move the failures into a subfolder.
LABSCO SUMMARY
Everything is measured rather than judged: sharpness, focus and exposure come out as numbers, which is exactly why the culling tools can be trusted with a threshold and why they only move the images that fall below it. Know what it is not — it has no view on aesthetics or intent, so a deliberately soft, dark frame will score badly and be culled unless you set the threshold with that in mind.
A local photo quality analyser. Computer vision models run on your own machine over folders of images and return numeric scores per metric, so culling and burst selection become calls rather than an evening in a photo editor.
Nothing to sign up for and nothing uploaded — the models run locally, which is also what makes speed a function of your hardware. Python 3.10 or newer; it starts as uvx photographi-mcp, and uvx --refresh photographi-mcp pulls a newer build. It needs read access to the photo folders and write access wherever culled files land. On Apple silicon the vendor measures the YOLO model loading in about 0.38s once, then roughly 0.19s per image in fast mode, which downsamples large frames, against about 1.50s per image with fast_mode=False at full resolution. Anonymous aggregate telemetry is on by default and carries no filenames, paths or EXIF; PHOTOGRAPHI_TELEMETRY_DISABLED=1 or the --disable-telemetry flag turns it off. MIT.
One command — uvx photographi-mcp
USE CASES
Rank the frames by technical quality and read the metrics behind the top ones, instead of pixel-peeping each shot in turn.
Move everything below the threshold into a subfolder and leave the rest untouched, in pages of 100 images at a time.
Batch analyse the whole folder for quality scores and verdicts per image, then inspect the ones that came back borderline.
Subject detection meters against what the photo is actually of, so a sharp background behind a soft face is scored as the miss it is.
Get object labels per image for content indexing, so a folder can be searched by what is in the frame rather than by filename.
Extract dominant colours as hex codes from one image, or across a folder, for a moodboard or a site palette.
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
photographi_analyze_folderBatch analyzes all images in a folder with high concurrency (4-8 images at once). Returns quality scores and judgements for each image. Default limit is 100 images per call. Use 'limit' and 'offset' for pagination. If 'nextOffset' is in the response, call again with that offset. Perfect for understanding the overall quality distribution of a shoot.
photographi_analyze_photoAnalyzes a single photo for technical quality (sharpness, exposure, noise, etc.). Returns overall score, judgement, and per-metric breakdowns. Use this for detailed inspection of individual images. For batch analysis, use photographi_analyze_folder.
photographi_cull_photographsCulls low-quality images by moving them to a 'culled_photos' subfolder. Images ABOVE the threshold (default 0.4) stay in the original folder. Images BELOW the threshold are moved to culled_photos/. Processes 100 images per call by default. Use 'limit' and 'offset' for pagination. Mode options: 'move' (default), 'xmp' (sidecar tagging), or 'both'.
photographi_get_color_paletteExtracts a representative color palette from a single image using K-Means clustering. Returns a list of hex color codes (e.g., ['#FF5733', '#33FF57', ...]). Default is 5 colors. Use this for design inspiration or color grading reference.
photographi_get_folder_palettesExtracts color palettes from multiple images in a folder. Processes 20 images per call by default. Use 'limit' and 'offset' for pagination. Returns a map of filename → color palette. Useful for building color-based galleries or moodboards.
photographi_get_scene_contentDetects objects in a photo using YOLO (e.g., person, dog, car, etc.). Returns a simple list of object labels. Use this for quick scene indexing or content-based search. For full quality analysis with subject-aware metering, use photographi_analyze_photo.
photographi_rank_photographsRanks photos by technical quality and returns the top_n best images. Uses high concurrency to process up to 100 images per call (default limit). Ideal for burst sequences or finding hero shots. Returns detailed metrics for each top image. For full folder analysis, use photographi_analyze_folder instead.
photographi_threshold_cullBinary threshold culling: moves images below min_confidence (default 0.6) to a 'rejects' subfolder. Images ABOVE the threshold stay in the original folder. Similar to photographi_cull_photographs, but uses a higher default threshold and 'rejects/' folder name for stricter filtering. Processes 100 images per call. Mode options: 'move', 'xmp', or 'both'.
BEFORE YOU INSTALL
8 tools when we connected. The real names carry a photographi_ prefix (photographi_analyze_photo); the unprefixed names in the documentation's table will not be found. photographi_cull_photographs and photographi_threshold_cull move files on disk.
Python 3.10+
CONTEXT COST
Rated by the size of the 8 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
uvx photographi-mcpclaude mcp add --scope user photographi uvx photographi-mcp{
"mcpServers": {
"photographi": {
"command": "uvx",
"args": [
"photographi-mcp"
]
}
}
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