{"name":"io.github.prasadabhishek/photographi-mcp","slug":"prasadabhishek-photographi-mcp","title":null,"description":"Visual Intelligence Command Center: A Local Computer Vision Engine for Photo Libraries","url":"https://mcp.market/server/prasadabhishek-photographi-mcp","rating":null,"grade":"B","score":75,"certified":false,"status":"active","category":"media","tags":["media"],"presence":{"score":10,"stars":5,"forks":1,"downloads_week":null,"last_push_at":"2026-02-16T01:55:54.000Z","license":"MIT"},"uptime":null,"claimed":false,"transport":"pypi","callable_via_gateway":false,"default_price_micros":0,"repository":"https://github.com/prasadabhishek/photographi-mcp","website":null,"version":"0.2.6","remotes":[],"packages":[{"registryType":"pypi","identifier":"photographi-mcp","version":"0.2.6","transport":{"type":"stdio"}}],"tools":[{"name":"photographi_analyze_folder","description":"Batch 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.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_analyze_photo","description":"Analyzes 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.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_cull_photographs","description":"Culls 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'.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_get_color_palette","description":"Extracts 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.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_get_folder_palettes","description":"Extracts 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.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_get_scene_content","description":"Detects 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.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_rank_photographs","description":"Ranks 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.","write_action":false,"price_micros":0,"input_schema":null},{"name":"photographi_threshold_cull","description":"Binary 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'.","write_action":false,"price_micros":0,"input_schema":null}],"scan":{"score":75,"grade":"B","scanned_at":"2026-09-28T14:27:53.217Z","report":{"scannerVersion":"0.1.10","scannedAt":"2026-09-28T14:27:53.197Z","components":{"code":{"score":25,"max":25,"notes":["3 source files scanned"]},"reliability":{"score":-1,"max":20,"notes":["no gateway calls yet and no remote to probe"]},"poisoning":{"score":-1,"max":15,"notes":["tools not inspected (local package is not executed); not counted"]},"auth":{"score":12,"max":15,"notes":["local package, no credentials required"]},"maintenance":{"score":4,"max":15,"notes":["last push 225 days ago"]},"identity":{"score":8,"max":10,"notes":["registry namespace matches repository owner","GitHub account older than a year"]}},"findings":[],"inputs":{"packages":[{"registryType":"pypi","identifier":"photographi-mcp","version":"0.2.6","found":true,"dependencyCount":6,"publishedAt":"2026-02-16T01:57:56.508607Z"}],"repo":{"found":true,"owner":"prasadabhishek","repo":"photographi-mcp","archived":false,"pushedAt":"2026-02-16T01:55:54Z","stars":5,"forks":1,"openIssues":0,"ownerType":"User","ownerAvatarUrl":"https://avatars.githubusercontent.com/u/6366423?v=4","ownerCreatedAt":"2014-01-10T06:12:55Z","license":"MIT"},"icon":{"url":"https://avatars.githubusercontent.com/u/6366423?v=4&s=128","source":"github"},"presence":{"stars":5,"forks":1,"downloadsWeek":null,"license":"MIT","lastPushAt":"2026-02-16T01:55:54.000Z","score":10}}}},"grade_history":[],"reviews":[]}