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Implements the MCP server protocol to register a callable tool with schema validation, allowing agents to request screenshots with optional parameters (window ID, region bounds, format). Handles tool invocation routing and response serialization back to the agent.","intents":["I want my AI agent to be able to request screenshots as part of its reasoning loop","I need to integrate screenshot capability into an existing MCP-based agent framework","I want to standardize how agents request visual context across different tools"],"best_for":["MCP-compatible AI agents (Claude, custom LLM agents)","developers building multi-tool agent systems","teams standardizing on MCP for tool orchestration"],"limitations":["Requires MCP client support — not compatible with non-MCP agents or REST-only integrations","Tool schema must be pre-defined — no dynamic parameter discovery at runtime","Single screenshot per invocation — batch screenshot requests require multiple tool calls","No built-in retry logic — agent must handle failed capture requests"],"requires":["MCP client implementation (e.g., Claude Desktop, Cline, custom MCP client)","MCP protocol version 1.0+","macOS 10.13+"],"input_types":["JSON tool invocation with optional parameters"],"output_types":["JSON response with base64-encoded image data","tool result with metadata (timestamp, window info)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screeny__cap_2","uri":"capability://safety.moderation.privacy.preserving.local.image.processing","name":"privacy-preserving local image processing","description":"Ensures all screenshot data remains on the local machine without transmission to external servers or cloud APIs. Implements a local-only architecture where image capture, storage, and delivery happen entirely within the MCP server process. No telemetry, no image logging to external services, and no intermediate cloud processing steps.","intents":["I need to ensure sensitive UI content (passwords, private data) never leaves my machine","I want to use AI agents for automation without uploading screenshots to cloud services","I need compliance with data residency requirements for regulated industries"],"best_for":["enterprises with strict data privacy policies","developers handling sensitive financial or healthcare UIs","teams in regulated industries (finance, healthcare, government)"],"limitations":["Requires local LLM or MCP client — cannot use cloud-only AI services without local proxy","No built-in encryption at rest — images stored in memory only, lost on process termination","No audit logging — cannot track which screenshots were captured or when","Limited to local machine resources — cannot scale to multiple machines without additional infrastructure"],"requires":["Local MCP client or local LLM deployment","macOS with standard security settings","No requirement for external API keys or cloud accounts"],"input_types":["none (privacy is a property, not an input)"],"output_types":["local image data (never transmitted)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screeny__cap_3","uri":"capability://image.visual.window.specific.screenshot.targeting","name":"window-specific screenshot targeting","description":"Allows agents to request screenshots of specific windows by window identifier or title matching, rather than capturing the entire screen. Implements window enumeration and filtering logic to locate target windows and capture only their content. Supports optional region-of-interest cropping to capture specific UI elements within a window.","intents":["I want my agent to focus on a specific application window without capturing other windows","I need to capture only a portion of a window (e.g., a form or dialog box)","I want to reduce image size and latency by capturing only relevant UI areas"],"best_for":["agents automating specific applications (browser, IDE, terminal)","developers building multi-window automation workflows","teams optimizing for bandwidth and processing speed"],"limitations":["Window targeting requires window ID or exact title match — fuzzy matching not supported","Cannot capture windows that are minimized or hidden behind other windows","Region-of-interest cropping requires pixel coordinates — no semantic element selection","Window enumeration may fail if accessibility permissions are not granted"],"requires":["macOS accessibility permissions enabled for MCP server","Window ID or window title string for targeting","macOS 10.13+"],"input_types":["window identifier (string or integer)","region bounds (optional, as x/y/width/height)"],"output_types":["cropped image data","window metadata (title, bounds, process name)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-screeny__cap_4","uri":"capability://image.visual.real.time.visual.feedback.loop.for.agent.actions","name":"real-time visual feedback loop for agent actions","description":"Enables agents to capture screenshots before and after taking actions (e.g., clicking buttons, typing text), creating a visual feedback loop for verification and error detection. Agents can request screenshots, take an action via another tool, then request another screenshot to verify the action succeeded. Supports sequential screenshot requests within a single agent reasoning step.","intents":["I want my agent to verify that a button click actually changed the UI","I need my agent to detect when an action failed and retry or take an alternative path","I want to create a visual audit trail of agent actions for debugging"],"best_for":["developers building robust UI automation agents","teams implementing error recovery in agent workflows","QA engineers creating visual regression testing with agents"],"limitations":["Requires synchronous action-screenshot-verification flow — asynchronous UI updates may not be captured","No built-in image comparison — agent must perform visual diff analysis itself","Screenshot timing is critical — too-fast captures may miss UI state changes, too-slow captures waste latency","No built-in wait-for-element logic — agent must implement polling or timeout logic"],"requires":["MCP client with support for sequential tool calls","Agent with vision capabilities to analyze screenshot differences","Deterministic UI behavior (no random animations or delays)"],"input_types":["screenshot request before action","action invocation (via separate tool)","screenshot request after action"],"output_types":["before/after image pairs","visual state comparison data"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"moderate","permissions":["macOS 10.13+","MCP client compatible with macOS (e.g., Claude Desktop)","Window must be accessible via macOS accessibility APIs","MCP client implementation (e.g., Claude Desktop, Cline, custom MCP client)","MCP protocol version 1.0+","Local MCP client or local LLM deployment","macOS with standard security settings","No requirement for external API keys or cloud accounts","macOS accessibility permissions enabled for MCP server","Window ID or window title string for targeting"],"failure_modes":["macOS-only — no Windows or Linux support","Requires explicit window focus or window ID specification — cannot capture background windows without system permissions","Screenshot latency depends on window size and GPU capabilities — large windows may add 100-500ms per capture","No built-in image compression — raw pixel data can be large (multi-megabyte for high-res displays)","Requires MCP client support — not compatible with non-MCP agents or REST-only integrations","Tool schema must be pre-defined — no dynamic parameter discovery at runtime","Single screenshot per invocation — batch screenshot requests require multiple tool calls","No built-in retry logic — agent must handle failed capture requests","Requires local LLM or MCP client — cannot use cloud-only AI services without local proxy","No built-in encryption at rest — images stored in memory only, lost on process termination","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":"2026-06-17T09:51:04.049Z","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=screeny","compare_url":"https://unfragile.ai/compare?artifact=screeny"}},"signature":"Ah1LjDZfw+1EoNr9CVNnIRbtpc7arUSqE7Qo+NzgK4X4F05PpmusDOc18wzKPofXCuAURqR2G5o7gWP4TDwIDw==","signedAt":"2026-06-21T12:55:05.539Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/screeny","artifact":"https://unfragile.ai/screeny","verify":"https://unfragile.ai/api/v1/verify?slug=screeny","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"}}