{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_ae14watanabe-huggingface-cloth-segmentation","slug":"ae14watanabe-huggingface-cloth-segmentation","name":"huggingface-cloth-segmentation","type":"mcp","url":"https://github.com/ae14watanabe/huggingface-cloth-segmentation","page_url":"https://unfragile.ai/ae14watanabe-huggingface-cloth-segmentation","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:ae14watanabe/huggingface-cloth-segmentation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_ae14watanabe-huggingface-cloth-segmentation__cap_0","uri":"capability://tool.use.integration.mcp.based.cloth.segmentation.model.serving","name":"mcp-based cloth segmentation model serving","description":"Exposes HuggingFace cloth segmentation models through the Model Context Protocol (MCP) standard, enabling client applications to invoke segmentation inference via standardized MCP tool calls. The server wraps pre-trained segmentation models (likely from the HuggingFace model hub) and translates MCP requests into model inference calls, returning segmentation masks or labeled regions. This allows any MCP-compatible client (Claude, custom agents, IDEs) to access cloth segmentation without direct model loading or dependency management.","intents":["I want to add cloth segmentation capabilities to my AI agent without managing model dependencies myself","I need to call a cloth segmentation model from Claude or another MCP client","I want to expose cloth segmentation as a standardized tool in my LLM application stack"],"best_for":["AI agent builders integrating computer vision into LLM workflows","Teams building fashion/retail applications with Claude or other MCP-compatible LLMs","Developers wanting to avoid model download/setup overhead by delegating to a server"],"limitations":["Network latency added for each segmentation request (inference happens on server, not locally)","Server availability required — no offline capability if server is down","Model selection and version management controlled by server maintainer, not client","Inference performance depends on server hardware, not client resources"],"requires":["MCP client implementation (Claude, custom MCP client, or compatible IDE)","Network connectivity to the MCP server","Python runtime on server (inferred from HuggingFace model loading patterns)","HuggingFace model hub access or pre-downloaded model weights"],"input_types":["image (JPEG, PNG, or base64-encoded)","image URL or file path"],"output_types":["segmentation mask (pixel-level labels)","structured JSON with clothing region boundaries","annotated image with overlaid segmentation"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ae14watanabe-huggingface-cloth-segmentation__cap_1","uri":"capability://image.visual.clothing.region.classification.and.labeling","name":"clothing region classification and labeling","description":"Segments input images into distinct clothing regions (e.g., shirt, pants, jacket, accessories) and assigns semantic labels to each region. The capability likely uses a pre-trained segmentation model from HuggingFace (possibly a U-Net or similar architecture) that outputs per-pixel class predictions, then aggregates connected components into labeled regions. Clients receive structured output mapping region IDs to clothing categories, enabling downstream applications to reason about garment composition.","intents":["I want to identify and label different clothing items in an image programmatically","I need to extract which clothing types are present in a photo for fashion recommendation","I want to analyze garment composition for inventory or styling applications"],"best_for":["Fashion/retail platforms building automated outfit analysis","E-commerce applications categorizing product images","Style recommendation engines needing garment-level understanding"],"limitations":["Accuracy depends on training data — may struggle with occluded, overlapping, or unusual clothing","Model trained on specific clothing taxonomy — custom clothing types not supported without retraining","Performance degrades on low-resolution or heavily compressed images","No real-time video segmentation — processes static images only"],"requires":["Input image with visible clothing items","MCP server running with cloth segmentation model loaded","Model weights compatible with HuggingFace transformers library"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["JSON with region labels and class names","segmentation mask with integer class IDs","bounding boxes per clothing region"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ae14watanabe-huggingface-cloth-segmentation__cap_2","uri":"capability://data.processing.analysis.image.preprocessing.and.normalization.for.segmentation","name":"image preprocessing and normalization for segmentation","description":"Automatically handles image preprocessing required by the cloth segmentation model, including resizing, normalization, and format conversion. The server likely implements standard computer vision preprocessing: loading images from various formats, resizing to model input dimensions (e.g., 512x512), normalizing pixel values to the model's expected range (e.g., [0, 1] or ImageNet normalization), and converting to tensor format. This abstraction shields clients from model-specific preprocessing details.","intents":["I want to send an image to the segmentation model without worrying about format or size","I need consistent preprocessing applied to all images before segmentation","I want to avoid implementing image loading and normalization logic in my client"],"best_for":["Developers building quick prototypes who want to skip preprocessing boilerplate","Applications handling diverse image sources (URLs, uploads, file paths) needing uniform handling","Teams wanting consistent preprocessing across multiple clients"],"limitations":["Fixed preprocessing pipeline — no customization of normalization or resizing strategy","Resizing may distort aspect ratios if not handled with padding/letterboxing","Large images require downsampling, potentially losing fine-grained clothing details","No support for batch preprocessing — images processed one at a time"],"requires":["Image in a supported format (JPEG, PNG, WebP, or base64-encoded)","Image dimensions reasonable for resizing (not extremely small or large)"],"input_types":["image file path","image URL","base64-encoded image data"],"output_types":["normalized tensor ready for model inference"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ae14watanabe-huggingface-cloth-segmentation__cap_3","uri":"capability://tool.use.integration.mcp.protocol.request.response.handling","name":"mcp protocol request-response handling","description":"Implements the Model Context Protocol server-side message handling, translating incoming MCP tool calls into segmentation inference requests and returning results in MCP-compliant format. The server likely uses an MCP SDK (e.g., mcp-python or similar) to handle protocol parsing, request routing, and response serialization. This enables any MCP client (Claude, custom agents) to discover the segmentation tool via MCP's tool definition mechanism and invoke it with structured arguments.","intents":["I want my MCP client (Claude, custom agent) to discover and call cloth segmentation as a tool","I need standardized request-response handling between my client and the segmentation server","I want to integrate cloth segmentation into an MCP-based agent framework"],"best_for":["Developers building MCP-compatible agents or applications","Teams standardizing on MCP for tool integration across their AI stack","Claude users wanting to add cloth segmentation capabilities via MCP"],"limitations":["MCP protocol overhead adds latency compared to direct function calls","Requires MCP client implementation — not compatible with non-MCP tools or APIs","Tool discovery and schema definition must be maintained in sync with actual implementation","No built-in request batching — each segmentation call is a separate MCP message"],"requires":["MCP-compatible client (Claude, custom MCP client library)","MCP server running and accessible (local or remote)","MCP SDK/library on server (e.g., mcp-python)"],"input_types":["MCP tool call with image parameter (string or base64)"],"output_types":["MCP tool result with segmentation output (JSON or structured data)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ae14watanabe-huggingface-cloth-segmentation__cap_4","uri":"capability://image.visual.model.loading.and.inference.execution","name":"model loading and inference execution","description":"Loads pre-trained cloth segmentation models from HuggingFace model hub and executes inference on input images. The server likely uses the HuggingFace transformers library to load model weights, instantiate the model architecture, and run forward passes. Inference is executed on available hardware (CPU or GPU if available), with results cached or streamed back to the client. This capability abstracts model initialization, device management, and inference orchestration.","intents":["I want to run cloth segmentation inference without managing model downloads or GPU setup","I need reliable, consistent inference results from a pre-trained model","I want to avoid loading the model multiple times for repeated segmentation requests"],"best_for":["Applications requiring consistent, pre-trained segmentation without custom model training","Teams wanting to offload inference to a dedicated server rather than client devices","Developers avoiding GPU/CUDA setup complexity by delegating to a server"],"limitations":["Inference latency depends on server hardware — GPU required for reasonable performance on large images","Model weights must be downloaded on first run (can be several hundred MB)","No model quantization or optimization — full precision inference may be slow on CPU","Memory overhead of keeping model loaded in VRAM/RAM between requests"],"requires":["Python 3.7+ with PyTorch or TensorFlow installed","HuggingFace transformers library","Sufficient disk space for model weights (typically 100-500 MB)","GPU recommended for reasonable inference speed (NVIDIA CUDA or compatible)"],"input_types":["preprocessed image tensor"],"output_types":["segmentation logits or class predictions","confidence scores per region"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"moderate","permissions":["MCP client implementation (Claude, custom MCP client, or compatible IDE)","Network connectivity to the MCP server","Python runtime on server (inferred from HuggingFace model loading patterns)","HuggingFace model hub access or pre-downloaded model weights","Input image with visible clothing items","MCP server running with cloth segmentation model loaded","Model weights compatible with HuggingFace transformers library","Image in a supported format (JPEG, PNG, WebP, or base64-encoded)","Image dimensions reasonable for resizing (not extremely small or large)","MCP-compatible client (Claude, custom MCP client library)"],"failure_modes":["Network latency added for each segmentation request (inference happens on server, not locally)","Server availability required — no offline capability if server is down","Model selection and version management controlled by server maintainer, not client","Inference performance depends on server hardware, not client resources","Accuracy depends on training data — may struggle with occluded, overlapping, or unusual clothing","Model trained on specific clothing taxonomy — custom clothing types not supported without retraining","Performance degrades on low-resolution or heavily compressed images","No real-time video segmentation — processes static images only","Fixed preprocessing pipeline — no customization of normalization or resizing strategy","Resizing may distort aspect ratios if not handled with padding/letterboxing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:25.062Z","last_scraped_at":"2026-05-03T15:19:25.721Z","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=ae14watanabe-huggingface-cloth-segmentation","compare_url":"https://unfragile.ai/compare?artifact=ae14watanabe-huggingface-cloth-segmentation"}},"signature":"RVnIo/bXPXy2CTqd/dEuwLILYTPPmdHw412IaE3c3frYTa6wzKnE1OKTx9S3/Spp/RXGdKd1YImxp9MTd5oUDw==","signedAt":"2026-06-21T02:59:20.483Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ae14watanabe-huggingface-cloth-segmentation","artifact":"https://unfragile.ai/ae14watanabe-huggingface-cloth-segmentation","verify":"https://unfragile.ai/api/v1/verify?slug=ae14watanabe-huggingface-cloth-segmentation","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"}}