{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_marvin","slug":"marvin","name":"Marvin","type":"product","url":"https://www.askmarvin.ai","page_url":"https://unfragile.ai/marvin","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_marvin__cap_0","uri":"capability://text.generation.language.unified.multi.modal.nlp.processing.with.model.abstraction","name":"unified multi-modal nlp processing with model abstraction","description":"Provides a single API surface for common NLP tasks (text classification, named entity recognition, sentiment analysis, summarization) by abstracting underlying model selection and inference logic. Routes requests to appropriate pre-trained models based on task type, handling tokenization, model loading, and result normalization transparently without exposing model-specific configuration to the developer.","intents":["I want to add sentiment analysis to my app without learning multiple NLP libraries","I need to extract entities from user input without managing model downloads and dependencies","I want to quickly prototype text classification without writing boilerplate for model initialization"],"best_for":["indie developers building MVP applications with NLP features","teams prototyping AI features without dedicated ML engineers","startups needing rapid iteration on text processing without infrastructure overhead"],"limitations":["No fine-tuning support — locked to pre-trained models, limiting domain-specific accuracy","Abstraction layer prevents access to model confidence scores, token-level outputs, or custom inference parameters","No control over model selection or versioning — underlying models may change without notice","Batch processing capabilities unknown — likely optimized for single-request latency rather than throughput"],"requires":["API key for Marvin service","Network connectivity for inference calls","HTTP client library (language-dependent SDK)"],"input_types":["plain text","unicode strings"],"output_types":["structured JSON with task-specific fields (e.g., sentiment scores, entity lists)","classification labels with confidence scores"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_1","uri":"capability://image.visual.image.analysis.and.classification.with.vision.model.abstraction","name":"image analysis and classification with vision model abstraction","description":"Accepts image inputs (URLs, file uploads, or base64-encoded data) and routes them through abstracted vision models for tasks like object detection, image classification, and visual content analysis. Handles image preprocessing, model inference, and structured result extraction without exposing underlying model architecture or requiring manual image normalization.","intents":["I want to detect objects in user-uploaded images without managing computer vision libraries","I need to classify images by content category without training a custom model","I want to extract visual features from images for recommendation or moderation workflows"],"best_for":["web and mobile app developers adding image analysis without computer vision expertise","teams building content moderation or recommendation systems on tight timelines","startups prototyping visual search or product recognition features"],"limitations":["No support for custom model training or fine-tuning on domain-specific image datasets","Latency overhead from cloud inference — not suitable for real-time video processing at high frame rates","Image size and format constraints unknown — likely limited to standard web formats (JPEG, PNG, WebP)","No access to intermediate model outputs (feature maps, attention weights) for interpretability","Batch processing of multiple images likely requires sequential API calls rather than vectorized inference"],"requires":["API key for Marvin service","Image input as URL, file path, or base64-encoded string","Network connectivity for cloud inference"],"input_types":["image URL","base64-encoded image data","local file path"],"output_types":["structured JSON with detected objects, bounding boxes, and confidence scores","classification labels with probability distributions","image metadata and visual feature vectors"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_2","uri":"capability://text.generation.language.audio.transcription.and.speech.to.text.with.model.abstraction","name":"audio transcription and speech-to-text with model abstraction","description":"Accepts audio files or streams and transcribes them to text using abstracted speech recognition models. Handles audio format normalization, model selection, and result post-processing (punctuation, capitalization) without requiring developers to manage audio codec libraries or speech model infrastructure.","intents":["I want to transcribe user voice input in my app without integrating with multiple speech APIs","I need to convert audio files to text for search or analysis without managing audio preprocessing","I want to add voice command recognition without building custom acoustic models"],"best_for":["mobile and web app developers adding voice features without audio engineering expertise","teams building voice-enabled search or accessibility features","startups prototyping voice-based interfaces for MVP validation"],"limitations":["No speaker diarization or multi-speaker separation — treats all audio as single speaker","No language detection — likely requires explicit language specification or defaults to English","No custom vocabulary or domain-specific terminology support — generic models may misrecognize technical terms","Latency unknown but likely 1-5 seconds for typical audio clips due to cloud inference","No streaming transcription for real-time voice input — batch processing only"],"requires":["API key for Marvin service","Audio file in supported format (MP3, WAV, OGG, FLAC, or similar)","Network connectivity for cloud inference"],"input_types":["audio file upload","audio URL","base64-encoded audio data"],"output_types":["plain text transcription","structured JSON with timestamps and confidence scores","alternative transcription hypotheses"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_3","uri":"capability://image.visual.video.processing.and.frame.analysis.with.temporal.abstraction","name":"video processing and frame analysis with temporal abstraction","description":"Processes video files by extracting frames and applying vision or audio analysis across temporal sequences. Abstracts frame sampling, model inference scheduling, and result aggregation to enable tasks like scene detection, activity recognition, or video summarization without requiring developers to manage video codec libraries or frame-by-frame processing loops.","intents":["I want to analyze video content for moderation or categorization without writing video processing code","I need to extract key frames or scenes from videos for thumbnail generation or summarization","I want to detect activities or objects across video sequences without managing temporal logic"],"best_for":["teams building video content platforms with moderation or recommendation features","startups prototyping video analysis features without computer vision infrastructure","developers adding video understanding to existing applications without video processing expertise"],"limitations":["Frame sampling strategy unknown — likely fixed intervals (e.g., every 5 frames) rather than adaptive keyframe detection","No temporal modeling — likely analyzes frames independently rather than using optical flow or 3D convolutions for motion understanding","High latency for long videos — processing time scales linearly with video duration and frame count","No streaming video support — requires complete file upload before processing begins","No fine-grained temporal annotations (e.g., activity start/end times) — likely returns scene-level or shot-level results only"],"requires":["API key for Marvin service","Video file in supported format (MP4, WebM, MOV, or similar)","Network connectivity and sufficient bandwidth for video upload","Patience for processing latency (likely minutes for typical videos)"],"input_types":["video file upload","video URL","base64-encoded video data"],"output_types":["frame-level analysis results (objects, scenes, activities)","video-level summaries (key frames, scene descriptions, activity timeline)","structured JSON with temporal metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_4","uri":"capability://tool.use.integration.unified.api.client.with.language.sdk.abstraction","name":"unified api client with language sdk abstraction","description":"Provides language-specific SDKs (Python, JavaScript, etc.) that abstract HTTP request construction, authentication, error handling, and response parsing for all Marvin capabilities. Implements request batching, retry logic, and rate-limit handling transparently, allowing developers to call NLP, vision, audio, and video functions with consistent method signatures across different modalities.","intents":["I want a consistent API interface across NLP, vision, audio, and video without learning different client libraries","I need automatic retry and error handling for API calls without writing boilerplate","I want to use Marvin in my existing Python or JavaScript project without managing HTTP details"],"best_for":["developers building multi-modal applications who want a unified interface","teams standardizing on a single AI toolkit to reduce dependency fragmentation","indie developers who want to minimize boilerplate and focus on application logic"],"limitations":["SDK language coverage unknown — likely Python and JavaScript only, no Go, Rust, or Java support","No offline mode — all operations require cloud connectivity and API calls","Retry logic and rate-limit handling likely opaque — no fine-grained control over backoff strategies","No built-in caching or local result memoization — repeated requests incur full API latency","Streaming response support unknown — likely request-response only, no server-sent events or WebSocket support"],"requires":["API key for Marvin service","Python 3.7+ or Node.js 14+ (language-dependent)","Network connectivity for API calls","pip or npm for SDK installation"],"input_types":["method calls with task-specific parameters","configuration objects for API credentials and options"],"output_types":["structured Python objects or JavaScript objects with task results","exceptions or error objects for failed requests"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.content","name":"structured data extraction from unstructured content","description":"Accepts unstructured text, images, or audio and extracts structured data (entities, relationships, key-value pairs) using language models or vision models with schema-based output formatting. Routes requests through appropriate models and enforces output schema validation, returning JSON-serializable results without requiring developers to parse or normalize model outputs manually.","intents":["I want to extract structured data from documents or images without writing parsing logic","I need to convert user input into a structured format for database storage or API calls","I want to validate that extracted data matches my application schema before processing"],"best_for":["teams building data extraction pipelines for document processing or form automation","developers adding structured data extraction to chatbots or voice interfaces","startups building data enrichment or knowledge graph construction features"],"limitations":["Schema definition format unknown — likely JSON Schema or similar, but custom validation rules may not be supported","Extraction accuracy depends on model capability — no fine-tuning on domain-specific data for improved precision","No confidence scores or uncertainty quantification — results returned as-is without reliability indicators","Hallucination risk — models may invent data not present in source material","No multi-step extraction workflows — single-pass extraction only, no iterative refinement"],"requires":["API key for Marvin service","Input content (text, image, or audio)","JSON Schema or similar format defining expected output structure"],"input_types":["plain text","image URL or file","audio file or URL"],"output_types":["structured JSON matching provided schema","validation errors if extraction fails schema validation"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_6","uri":"capability://safety.moderation.content.moderation.and.safety.filtering.across.modalities","name":"content moderation and safety filtering across modalities","description":"Analyzes text, images, audio, and video content to detect harmful, inappropriate, or policy-violating material. Routes content through moderation models that classify safety categories (hate speech, violence, adult content, etc.) and returns structured results with severity scores and recommended actions without requiring developers to implement custom content policies.","intents":["I want to filter user-generated content for harmful material without building custom moderation rules","I need to flag inappropriate images or videos in my platform without manual review","I want to detect hate speech or toxic language in user comments automatically"],"best_for":["platforms with user-generated content requiring automated moderation","teams building community features without dedicated trust and safety staff","startups needing rapid content filtering without custom model training"],"limitations":["Moderation policies are opaque — no visibility into what constitutes violation for each category","No customization of moderation rules — one-size-fits-all policies may not match application norms","False positive and false negative rates unknown — accuracy varies by content type and language","No appeal or override mechanism — flagged content cannot be easily reviewed or whitelisted","Cultural and contextual understanding limited — models may struggle with sarcasm, idioms, or regional norms"],"requires":["API key for Marvin service","Content input (text, image, audio, or video)","Network connectivity for cloud inference"],"input_types":["plain text","image URL or file","audio file or URL","video file or URL"],"output_types":["structured JSON with safety category scores (0-1 confidence per category)","recommended action (allow, flag, block)","explanation or reasoning for classification"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_7","uri":"capability://automation.workflow.batch.processing.with.asynchronous.job.management","name":"batch processing with asynchronous job management","description":"Accepts multiple inputs (texts, images, videos) for processing and returns job IDs for asynchronous execution. Implements polling or webhook callbacks to notify developers when results are ready, enabling efficient processing of large datasets without blocking on individual API calls. Abstracts job scheduling, status tracking, and result aggregation.","intents":["I want to process 1000 images without waiting for each one individually","I need to analyze a large video library overnight without blocking my application","I want to extract data from a batch of documents and get results when ready"],"best_for":["teams processing large datasets or bulk content analysis","applications with non-real-time processing requirements (overnight jobs, scheduled tasks)","developers building data pipelines or ETL workflows"],"limitations":["Batch API design unknown — may require specific input format or size limits","Webhook callback support unknown — may only support polling, adding latency to result retrieval","No progress tracking or partial results — likely returns all-or-nothing results when job completes","Job retention policy unknown — results may expire after fixed period (e.g., 24 hours)","No cost optimization — batch processing may not offer discounts vs. individual API calls"],"requires":["API key for Marvin service","Batch input file or array of items","Webhook endpoint (if using callback mode) or polling logic"],"input_types":["JSON array of items","CSV or JSONL file with batch inputs","S3 or cloud storage URI"],"output_types":["job ID for tracking","structured results file (JSON, CSV, or JSONL)","webhook callback with results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_marvin__cap_8","uri":"capability://automation.workflow.result.caching.and.memoization.with.content.based.deduplication","name":"result caching and memoization with content-based deduplication","description":"Automatically caches API results based on input content hash, returning cached results for identical or similar inputs without re-invoking models. Implements cache invalidation policies and allows developers to configure cache TTL and storage backend without managing cache infrastructure directly.","intents":["I want to avoid re-analyzing the same image or text multiple times","I need to reduce API costs by caching results for repeated requests","I want to speed up responses for common queries by serving cached results"],"best_for":["applications with repeated or similar requests (e.g., analyzing same user input multiple times)","teams optimizing API costs by reducing redundant model inference","developers building caching layers without managing Redis or Memcached"],"limitations":["Cache key strategy unknown — may use exact input hash only, missing semantic similarity opportunities","Cache storage backend unknown — likely cloud-hosted, adding latency vs. local caching","No cache invalidation control — developers cannot manually clear cache for specific inputs","Cache hit rates unknown — no visibility into cache effectiveness or storage usage","Privacy implications — cached results may be retained longer than desired for sensitive data"],"requires":["API key for Marvin service","Cache configuration (TTL, storage backend) if customization needed"],"input_types":["any input type (text, image, audio, video)"],"output_types":["cached results with metadata indicating cache hit","fresh results if cache miss"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["API key for Marvin service","Network connectivity for inference calls","HTTP client library (language-dependent SDK)","Image input as URL, file path, or base64-encoded string","Network connectivity for cloud inference","Audio file in supported format (MP3, WAV, OGG, FLAC, or similar)","Video file in supported format (MP4, WebM, MOV, or similar)","Network connectivity and sufficient bandwidth for video upload","Patience for processing latency (likely minutes for typical videos)","Python 3.7+ or Node.js 14+ (language-dependent)"],"failure_modes":["No fine-tuning support — locked to pre-trained models, limiting domain-specific accuracy","Abstraction layer prevents access to model confidence scores, token-level outputs, or custom inference parameters","No control over model selection or versioning — underlying models may change without notice","Batch processing capabilities unknown — likely optimized for single-request latency rather than throughput","No support for custom model training or fine-tuning on domain-specific image datasets","Latency overhead from cloud inference — not suitable for real-time video processing at high frame rates","Image size and format constraints unknown — likely limited to standard web formats (JPEG, PNG, WebP)","No access to intermediate model outputs (feature maps, attention weights) for interpretability","Batch processing of multiple images likely requires sequential API calls rather than vectorized inference","No speaker diarization or multi-speaker separation — treats all audio as single speaker","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.857Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=marvin","compare_url":"https://unfragile.ai/compare?artifact=marvin"}},"signature":"vxDaTOAopBo/eJbIqzLsFvDG3GS54HIuMuNLf4yeXSdstAeDY4REaIvIsuKkm05+ejbDo8+V7NLCXgyyCbzkAg==","signedAt":"2026-06-21T16:58:56.631Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/marvin","artifact":"https://unfragile.ai/marvin","verify":"https://unfragile.ai/api/v1/verify?slug=marvin","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"}}