{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-imagician","slug":"imagician","name":"Imagician","type":"mcp","url":"https://github.com/flowy11/imagician","page_url":"https://unfragile.ai/imagician","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-imagician__cap_0","uri":"capability://image.visual.mcp.native.image.resizing.with.dimension.aware.scaling","name":"mcp-native image resizing with dimension-aware scaling","description":"Exposes image resizing operations through the Model Context Protocol (MCP) server interface, allowing LLM agents and tools to invoke sharp's libvips-backed scaling engine with width/height/fit parameters. Implements MCP resource handlers that accept image paths or buffers and return resized outputs, enabling programmatic dimension transformation without direct library imports.","intents":["I need my LLM agent to resize images to specific dimensions as part of a batch processing workflow","I want to expose image resizing as a tool that Claude or other MCP-compatible models can call","I need to standardize image dimensions across a dataset before feeding to a vision model"],"best_for":["LLM application developers building multi-modal agents","Teams integrating image preprocessing into MCP-based tool chains","Developers needing headless image resizing without maintaining separate Python/Node services"],"limitations":["Resizing operations are synchronous — large batch operations may block the MCP server thread","No built-in progress reporting for long-running resize operations on very large images","Memory usage scales with image dimensions; no streaming support for gigapixel images"],"requires":["Node.js 16+","sharp library (native libvips bindings)","MCP client implementation (Claude Desktop, Cline, or custom MCP host)"],"input_types":["file path (string)","image buffer (Buffer)","base64-encoded image data"],"output_types":["resized image buffer","file written to disk","base64-encoded resized image"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_1","uri":"capability://image.visual.format.conversion.with.codec.aware.transcoding","name":"format conversion with codec-aware transcoding","description":"Converts images between multiple formats (JPEG, PNG, WebP, AVIF, GIF, TIFF, etc.) using sharp's codec abstraction layer, which selects optimal encoding parameters based on input/output format pairs. Exposes format conversion as MCP tools with quality/compression trade-off controls, allowing agents to choose output formats based on use-case constraints (file size, transparency support, animation).","intents":["Convert PNG images to WebP for web delivery with smaller file sizes","Transform JPEG photos to AVIF format for modern browser compatibility","Batch convert images to a standard format for downstream ML model ingestion"],"best_for":["Web developers optimizing image delivery pipelines","ML engineers standardizing image formats before model inference","Content platforms supporting multiple output formats for different clients"],"limitations":["AVIF encoding is slower than JPEG/PNG due to codec complexity; may add 500ms+ per image on CPU-constrained systems","Animated GIF to WebP conversion loses frame timing metadata without explicit preservation logic","No automatic format selection based on image content analysis — requires explicit format specification"],"requires":["Node.js 16+","sharp with libvips compiled for target formats (AVIF support requires libheif)","MCP client"],"input_types":["image file path","image buffer","base64 image data"],"output_types":["converted image buffer","file written to disk","base64-encoded output"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_2","uri":"capability://image.visual.intelligent.image.cropping.with.region.specification","name":"intelligent image cropping with region specification","description":"Crops images to specified rectangular regions using coordinate-based or gravity-based (center, top-left, etc.) anchor points. Sharp's crop implementation operates on the decoded image buffer in memory, allowing sub-pixel precision and chained operations. MCP interface accepts crop parameters (x, y, width, height) or gravity keywords, enabling agents to extract regions of interest without external coordinate calculation.","intents":["Extract a specific region from a screenshot for OCR or analysis","Crop product images to remove white space and center the subject","Programmatically extract face regions from photos for facial recognition preprocessing"],"best_for":["Computer vision pipeline builders needing region extraction","Content moderation systems cropping images for analysis","E-commerce platforms standardizing product image composition"],"limitations":["Cropping coordinates must be pre-calculated — no built-in object detection to find regions of interest","Out-of-bounds crop regions are silently clamped rather than raising errors, which may hide coordinate bugs","No support for non-rectangular crops (circular, polygonal) — requires post-processing with masking"],"requires":["Node.js 16+","sharp library","MCP client"],"input_types":["image file path","image buffer","crop parameters (x, y, width, height or gravity keyword)"],"output_types":["cropped image buffer","file written to disk"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_3","uri":"capability://image.visual.lossy.and.lossless.image.compression.with.quality.tuning","name":"lossy and lossless image compression with quality tuning","description":"Applies compression algorithms (JPEG quality reduction, PNG optimization, WebP/AVIF quality settings) to reduce file size while controlling visual degradation. Sharp exposes quality parameters (0-100 scale) that map to codec-specific compression levels. MCP tools allow agents to compress images with explicit quality targets, enabling trade-offs between file size and perceptual quality for different delivery contexts.","intents":["Reduce image file sizes for faster web delivery while maintaining acceptable visual quality","Compress images for storage in bandwidth-constrained environments","Batch compress a photo library to meet storage quotas"],"best_for":["Web performance engineers optimizing image delivery","Cloud storage platforms managing image archives","Mobile app developers minimizing bandwidth usage"],"limitations":["Quality parameter semantics differ across codecs (JPEG quality 80 ≠ WebP quality 80 perceptually) — requires testing per format","Lossless PNG compression is CPU-intensive; may add 1-2 seconds per large image on single-threaded execution","No perceptual quality metrics (SSIM, LPIPS) — quality is specified numerically without visual feedback"],"requires":["Node.js 16+","sharp library","MCP client"],"input_types":["image file path","image buffer","quality parameter (0-100)"],"output_types":["compressed image buffer","file written to disk","compression ratio metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_4","uri":"capability://data.processing.analysis.metadata.extraction.and.exif.data.handling","name":"metadata extraction and exif data handling","description":"Extracts image metadata (dimensions, color space, DPI, EXIF tags, ICC profiles) using sharp's metadata parsing without decoding the full image. Supports EXIF orientation correction to automatically rotate images based on camera orientation tags. MCP interface exposes metadata as structured JSON, enabling agents to inspect image properties before processing or make decisions based on EXIF data.","intents":["Check image dimensions before resizing to avoid upscaling","Extract camera model and settings from photo EXIF data","Automatically correct image orientation based on device rotation metadata"],"best_for":["Photo management applications needing EXIF inspection","ML pipelines requiring image property validation before inference","Mobile apps auto-rotating images based on device orientation"],"limitations":["EXIF parsing is format-specific; non-JPEG formats (PNG, WebP) have limited metadata support","Sensitive EXIF data (GPS coordinates, timestamps) is exposed without built-in redaction — requires explicit filtering","Orientation correction is applied during encoding, not metadata modification — original EXIF tags remain unchanged"],"requires":["Node.js 16+","sharp library","MCP client"],"input_types":["image file path","image buffer"],"output_types":["JSON object with metadata (width, height, space, density, exif, icc)","oriented image buffer (if correction applied)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_5","uri":"capability://automation.workflow.batch.image.processing.with.chained.operations","name":"batch image processing with chained operations","description":"Chains multiple image operations (resize → compress → convert format) into a single processing pipeline that executes sequentially on the decoded buffer. Sharp's fluent API allows composing operations without intermediate file writes. MCP implementation exposes batch operations as single tool calls, reducing round-trips and enabling atomic multi-step transformations that agents can invoke as a single unit.","intents":["Process 1000 images with a standard pipeline (resize to 800px, convert to WebP, compress to quality 75)","Apply a consistent transformation sequence to images without writing intermediate files","Reduce latency by combining multiple operations into a single MCP call"],"best_for":["Batch image processing systems handling large datasets","Content delivery networks optimizing images at ingestion time","ML data preparation pipelines standardizing image preprocessing"],"limitations":["Chained operations are sequential — no parallelization within a single image pipeline","Memory usage accumulates with operation count; very long chains (10+ operations) may exceed buffer limits on resource-constrained systems","Error handling is all-or-nothing; if operation N fails, the entire pipeline fails without partial results"],"requires":["Node.js 16+","sharp library","MCP client"],"input_types":["image file path or buffer","array of operation specifications (resize, compress, convert, etc.)"],"output_types":["processed image buffer","file written to disk"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_6","uri":"capability://image.visual.thumbnail.generation.with.aspect.ratio.preservation","name":"thumbnail generation with aspect-ratio preservation","description":"Generates thumbnails by combining resize, crop, and format conversion operations with aspect-ratio-aware scaling. Sharp's thumbnail implementation uses a 'cover' fit mode that scales to fill a bounding box while preserving aspect ratio, then crops excess. MCP interface accepts thumbnail dimensions and returns optimized small images suitable for UI display or search result previews.","intents":["Generate consistent thumbnail previews for a photo gallery","Create square thumbnails for social media sharing","Produce multiple thumbnail sizes (100px, 200px, 400px) from a single source image"],"best_for":["Web applications displaying image galleries or search results","Social media platforms generating preview thumbnails","E-commerce sites creating product thumbnail variations"],"limitations":["Aspect ratio preservation may crop important content if source image has extreme aspect ratios (very wide or tall)","No content-aware cropping — crops are centered, potentially removing subjects at image edges","Generating many thumbnail sizes requires multiple MCP calls or a batch operation"],"requires":["Node.js 16+","sharp library","MCP client"],"input_types":["image file path or buffer","thumbnail width and height"],"output_types":["thumbnail image buffer","file written to disk"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_7","uri":"capability://image.visual.animated.image.frame.extraction.and.manipulation","name":"animated image frame extraction and manipulation","description":"Extracts individual frames from animated formats (GIF, WebP with animation) and provides frame-level metadata (duration, disposal method). Sharp's animated image support allows iterating over frames and applying transformations per-frame. MCP tools expose frame extraction and re-encoding with frame duration control, enabling agents to manipulate animations programmatically.","intents":["Extract the first frame of an animated GIF as a static preview image","Reduce animation file size by removing frames or extending frame duration","Convert animated GIF to animated WebP with smaller file size"],"best_for":["Content platforms managing animated image assets","Web performance engineers optimizing animated content delivery","Media processing pipelines handling mixed static and animated images"],"limitations":["Frame extraction requires decoding all frames into memory — large animations (100+ frames) may exceed available RAM","Frame-level transformations (e.g., resize each frame differently) require iterating frames sequentially, adding latency","GIF to WebP animation conversion may lose color palette optimizations specific to GIF format"],"requires":["Node.js 16+","sharp library with animation support","MCP client"],"input_types":["animated image file path or buffer","frame index or extraction parameters"],"output_types":["extracted frame as static image buffer","re-encoded animation with modified frame durations","frame metadata (duration, disposal)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_8","uri":"capability://image.visual.color.space.conversion.and.icc.profile.handling","name":"color space conversion and icc profile handling","description":"Converts images between color spaces (sRGB, Adobe RGB, Lab, etc.) and applies or removes ICC color profiles. Sharp's color space support allows specifying input and output color spaces, enabling accurate color reproduction across different display contexts. MCP interface exposes color space conversion as a tool, allowing agents to ensure color consistency in multi-device workflows.","intents":["Convert images from Adobe RGB to sRGB for web display","Remove embedded ICC profiles to reduce file size","Ensure consistent color rendering across print and digital workflows"],"best_for":["Photography and design platforms managing color-critical workflows","Print production systems converting images to print color spaces","Web platforms ensuring consistent color across devices"],"limitations":["Color space conversion accuracy depends on ICC profile quality — missing or incorrect profiles may produce unexpected results","Conversion between perceptually different color spaces (sRGB to Lab) requires complex matrix operations, adding latency","No built-in color space detection — input color space must be specified explicitly"],"requires":["Node.js 16+","sharp library with color profile support","MCP client"],"input_types":["image file path or buffer","source and target color space names"],"output_types":["color-converted image buffer","file with embedded ICC profile"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-imagician__cap_9","uri":"capability://image.visual.watermark.and.overlay.composition","name":"watermark and overlay composition","description":"Composites watermarks, logos, or other overlay images onto source images using sharp's composite operation. Supports positioning (top-left, center, bottom-right, etc.), opacity control, and blending modes. MCP tools expose watermarking as a declarative operation, allowing agents to apply branding or copyright marks to images programmatically.","intents":["Add a watermark logo to all images in a batch before publishing","Overlay a semi-transparent copyright notice on photos","Composite multiple images (e.g., product photo + badge) into a single output"],"best_for":["Content platforms protecting intellectual property with watermarks","E-commerce sites adding badges or overlays to product images","Photography services branding client deliverables"],"limitations":["Overlay positioning is absolute (pixel coordinates or gravity keywords) — no content-aware placement that avoids important image regions","Blending modes are limited to those supported by libvips (multiply, screen, overlay, etc.) — custom blend functions not supported","Opacity is applied uniformly to the entire overlay — no per-pixel opacity control"],"requires":["Node.js 16+","sharp library","overlay image file or buffer","MCP client"],"input_types":["source image file path or buffer","overlay image file path or buffer","positioning parameters (gravity or x, y coordinates)","opacity (0-1)"],"output_types":["composited image buffer","file written to disk"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 16+","sharp library (native libvips bindings)","MCP client implementation (Claude Desktop, Cline, or custom MCP host)","sharp with libvips compiled for target formats (AVIF support requires libheif)","MCP client","sharp library","sharp library with animation support","sharp library with color profile support","overlay image file or buffer"],"failure_modes":["Resizing operations are synchronous — large batch operations may block the MCP server thread","No built-in progress reporting for long-running resize operations on very large images","Memory usage scales with image dimensions; no streaming support for gigapixel images","AVIF encoding is slower than JPEG/PNG due to codec complexity; may add 500ms+ per image on CPU-constrained systems","Animated GIF to WebP conversion loses frame timing metadata without explicit preservation logic","No automatic format selection based on image content analysis — requires explicit format specification","Cropping coordinates must be pre-calculated — no built-in object detection to find regions of interest","Out-of-bounds crop regions are silently clamped rather than raising errors, which may hide coordinate bugs","No support for non-rectangular crops (circular, polygonal) — requires post-processing with masking","Quality parameter semantics differ across codecs (JPEG quality 80 ≠ WebP quality 80 perceptually) — requires testing per format","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=imagician","compare_url":"https://unfragile.ai/compare?artifact=imagician"}},"signature":"RjRg27E6xjHolADdnrZABvx11SlGpp238E9/wt4BmE1gniX1FhD3ICr7H09hkUvzmI9dF1qL+tz1+syTIrwoCA==","signedAt":"2026-06-21T17:26:01.120Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/imagician","artifact":"https://unfragile.ai/imagician","verify":"https://unfragile.ai/api/v1/verify?slug=imagician","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}