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The MCP server implements an ensemble detection approach that evaluates linguistic markers, entropy patterns, and model-specific artifacts across different AI systems (GPT, Claude, etc.), returning confidence scores and detailed analysis rather than binary classifications.","intents":["Detect whether submitted text was written by an AI language model or human author","Identify specific AI models likely used to generate suspicious text passages","Get confidence scores and reasoning for AI detection decisions in academic/professional contexts","Integrate AI detection into content moderation pipelines for user-generated content platforms"],"best_for":["Educational institutions implementing academic integrity systems","Content platforms screening for AI-generated spam or policy violations","Enterprise teams building internal content verification workflows","Developers building LLM-powered applications needing detection guardrails"],"limitations":["Detection accuracy varies by text length — shorter passages (<100 words) have higher false positive rates","Requires continuous model updates as new AI systems and evasion techniques emerge","Cannot distinguish between human-written text and AI-generated text that has been heavily edited by humans","No built-in support for non-English languages beyond basic Latin-script detection"],"requires":["MCP client compatible with stdio or SSE transport","Network access to Winston AI detection backend API","Text input between 50-50000 characters for optimal accuracy"],"input_types":["plain text","markdown","HTML-stripped text"],"output_types":["JSON with detection confidence score (0-100)","detected AI model identifiers","linguistic analysis metadata","detailed reasoning for detection decision"],"categories":["safety-moderation","content-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-winston-ai__cap_1","uri":"capability://image.visual.ai.generated.image.detection.with.visual.artifact.analysis","name":"ai-generated image detection with visual artifact analysis","description":"Analyzes image files to detect AI-generated or AI-manipulated visual content by examining pixel-level artifacts, compression patterns, and structural inconsistencies characteristic of diffusion models and GANs. The detector processes images through multiple computer vision analysis layers including frequency domain analysis, semantic consistency checking, and known AI generation fingerprints to return detection confidence and visual evidence regions.","intents":["Identify whether an image was generated by AI image models (DALL-E, Midjourney, Stable Diffusion) or is authentic photography","Detect AI-manipulated or deepfake-edited regions within otherwise authentic images","Flag suspicious images in content moderation workflows before human review","Extract visual evidence regions that indicate AI generation for transparency and user education"],"best_for":["Social media platforms implementing visual content authenticity verification","News organizations screening for AI-generated or manipulated imagery","E-commerce platforms detecting AI-generated product images","Developers building content verification tools for misinformation detection"],"limitations":["Detection accuracy decreases significantly for heavily compressed or low-resolution images (<256px)","Cannot reliably detect AI images that have been post-processed with filters or artistic effects","Requires full image file — cannot analyze images from URLs or streaming sources","False positive rate increases for synthetic but non-AI-generated images (CGI, renders, screenshots)"],"requires":["MCP client with file I/O capabilities","Image files in JPEG, PNG, or WebP format","Minimum image dimensions of 256x256 pixels","Network access to Winston AI image analysis backend"],"input_types":["JPEG images","PNG images","WebP images","image file paths"],"output_types":["JSON with detection confidence score (0-100)","bounding boxes for suspicious regions","artifact type classification (diffusion model, GAN, deepfake, etc.)","detailed visual analysis metadata"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-winston-ai__cap_2","uri":"capability://safety.moderation.plagiarism.detection.with.source.attribution.and.similarity.scoring","name":"plagiarism detection with source attribution and similarity scoring","description":"Scans submitted text against a distributed database of academic papers, published content, and web sources to identify plagiarized passages and calculate overall similarity scores. The system uses semantic similarity matching (not just string matching) to detect paraphrased plagiarism, returning detailed reports with matched source citations, similarity percentages per passage, and recommendations for proper attribution.","intents":["Check student submissions for plagiarism before grading or acceptance","Identify improperly attributed or paraphrased content in academic papers","Detect copied content in blog posts, articles, or published materials","Generate detailed plagiarism reports with source citations for institutional records"],"best_for":["Educational institutions with plagiarism detection requirements","Academic journals and publishers screening submissions","Content agencies verifying original authorship of written work","Developers integrating plagiarism checking into learning management systems"],"limitations":["Semantic matching may produce false positives for common phrases or standard terminology in specialized fields","Database coverage limited to indexed sources — unpublished or paywalled content may not be detected","Requires minimum text length of 100 words for reliable plagiarism detection","Detection latency increases with text length (50KB+ documents may take 10-30 seconds)"],"requires":["MCP client with network connectivity","Text input between 100-500000 characters","API access to Winston AI plagiarism database","Optional: institutional database credentials for private source matching"],"input_types":["plain text","markdown","HTML-stripped text"],"output_types":["JSON with overall similarity percentage","array of matched sources with URLs and citations","per-passage similarity scores and highlighted regions","plagiarism report suitable for institutional records"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-winston-ai__cap_3","uri":"capability://tool.use.integration.mcp.server.integration.with.stdio.and.sse.transport.protocols","name":"mcp server integration with stdio and sse transport protocols","description":"Exposes AI detection and plagiarism checking capabilities as a Model Context Protocol (MCP) server supporting both stdio and Server-Sent Events (SSE) transport mechanisms. The server implements the MCP specification for tool registration, request/response handling, and error propagation, allowing any MCP-compatible client (Claude, custom agents, LLM applications) to invoke detection functions as native tools with structured input/output schemas.","intents":["Integrate Winston AI detection capabilities into Claude or other MCP-compatible LLM applications","Build custom AI agents that can check text/images for AI generation and plagiarism as part of larger workflows","Add detection tools to existing LLM applications without custom API integration code","Enable multi-tool orchestration where detection results inform downstream decision-making"],"best_for":["Developers building LLM agents with content verification requirements","Teams using Claude with custom tool integrations","Organizations deploying MCP-based AI application architectures","Builders creating multi-tool orchestration systems for content moderation"],"limitations":["Requires MCP client implementation — not compatible with REST-only API consumers","SSE transport limited to unidirectional server-to-client communication — requires polling for long-running operations","No built-in request queuing or rate limiting — relies on client-side throttling","Tool schema validation errors return generic MCP error responses without detailed debugging context"],"requires":["MCP-compatible client (Claude, custom MCP client library)","Node.js 18+ or Python 3.9+ runtime","Network connectivity between client and MCP server","Valid Winston AI API credentials for backend detection services"],"input_types":["MCP tool call requests with JSON parameters","text content (for AI detection and plagiarism checking)","image file paths or base64-encoded image data"],"output_types":["MCP tool result responses with JSON structured data","detection confidence scores and analysis metadata","error responses with MCP-compliant error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-winston-ai__cap_4","uri":"capability://automation.workflow.batch.processing.with.asynchronous.job.submission.and.result.polling","name":"batch processing with asynchronous job submission and result polling","description":"Supports submission of multiple detection jobs (text or image analysis) as a batch with asynchronous processing and result polling via job IDs. The server queues batch requests, processes them in the background, and allows clients to poll for completion status and retrieve results without blocking. This enables efficient processing of large document sets or image collections without timeout constraints.","intents":["Submit 100+ documents for plagiarism checking without waiting for sequential processing","Batch-check a folder of images for AI generation in a single operation","Monitor detection job progress and retrieve results when ready","Integrate batch detection into scheduled workflows or CI/CD pipelines"],"best_for":["Educational institutions processing semester-end plagiarism checks on hundreds of submissions","Content platforms screening bulk user-generated content uploads","Developers building batch processing workflows in CI/CD or data pipelines","Teams needing asynchronous detection without blocking application logic"],"limitations":["Job results retained only for 24-48 hours — clients must poll and retrieve results within retention window","No built-in progress callbacks — clients must implement polling loops to check job status","Batch size limited to 1000 items per submission to prevent backend overload","Asynchronous processing adds 5-30 second latency before results are available depending on queue depth"],"requires":["MCP client capable of making multiple sequential requests","Job IDs returned from batch submission for result polling","Network connectivity for polling requests (recommend 5-10 second polling intervals)","Winston AI API credentials with batch processing enabled"],"input_types":["array of text strings for batch plagiarism detection","array of image file paths for batch AI detection","mixed batches of text and image items"],"output_types":["job ID for tracking batch submission","job status responses (queued, processing, completed, failed)","array of detection results matching input batch order","per-item error details if individual items fail"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["MCP client compatible with stdio or SSE transport","Network access to Winston AI detection backend API","Text input between 50-50000 characters for optimal accuracy","MCP client with file I/O capabilities","Image files in JPEG, PNG, or WebP format","Minimum image dimensions of 256x256 pixels","Network access to Winston AI image analysis backend","MCP client with network connectivity","Text input between 100-500000 characters","API access to Winston AI plagiarism database"],"failure_modes":["Detection accuracy varies by text length — shorter passages (<100 words) have higher false positive rates","Requires continuous model updates as new AI systems and evasion techniques emerge","Cannot distinguish between human-written text and AI-generated text that has been heavily edited by humans","No built-in support for non-English languages beyond basic Latin-script detection","Detection accuracy decreases significantly for heavily compressed or low-resolution images (<256px)","Cannot reliably detect AI images that have been post-processed with filters or artistic effects","Requires full image file — cannot analyze images from URLs or streaming sources","False positive rate increases for synthetic but non-AI-generated images (CGI, renders, screenshots)","Semantic matching may produce false positives for common phrases or standard terminology in specialized fields","Database coverage limited to indexed sources — unpublished or paywalled content may not be detected","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-06-17T09:51:04.690Z","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=winston-ai","compare_url":"https://unfragile.ai/compare?artifact=winston-ai"}},"signature":"QpV5vMi+hsq9yxfY3XXV/JTkEQ6lT4BVon6MbZZURKkH4UjIc1Gd7pyNpFF1WMDLH1sIjZB+pZFYIK5QC1SJAw==","signedAt":"2026-06-22T18:00:49.216Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/winston-ai","artifact":"https://unfragile.ai/winston-ai","verify":"https://unfragile.ai/api/v1/verify?slug=winston-ai","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"}}