{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_alexanderlamb-domestic-motion","slug":"alexanderlamb-domestic-motion","name":"domestic-motion","type":"mcp","url":"https://smithery.ai/servers/alexanderlamb/domestic-motion","page_url":"https://unfragile.ai/alexanderlamb-domestic-motion","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:alexanderlamb/domestic-motion"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_alexanderlamb-domestic-motion__cap_0","uri":"capability://tool.use.integration.motion.sensor.event.streaming.via.mcp","name":"motion-sensor-event-streaming-via-mcp","description":"Exposes real-time motion detection events from domestic IoT sensors through the Model Context Protocol, allowing LLM agents to subscribe to and react to motion triggers in home environments. Implements MCP resource subscription patterns to stream sensor state changes with low-latency event delivery, enabling agents to build context-aware automation workflows based on physical motion events.","intents":["Stream motion sensor events from smart home devices into an LLM agent for real-time decision making","Trigger automated responses when motion is detected in specific rooms or zones","Build home automation workflows that react to human movement patterns","Monitor and log motion events for security or occupancy tracking"],"best_for":["Home automation developers building LLM-powered smart home agents","Teams integrating motion sensors with Claude or other MCP-compatible LLMs","Builders creating context-aware home assistants that respond to physical presence"],"limitations":["Requires compatible motion sensor hardware with network connectivity","Event latency depends on underlying sensor polling frequency and network conditions","No built-in event persistence or replay — events are ephemeral unless explicitly captured by the agent","Limited to motion detection; does not provide sensor calibration, sensitivity tuning, or multi-modal sensor fusion"],"requires":["MCP-compatible client (Claude desktop, LLM framework with MCP support)","Motion sensor hardware with network API or local integration","Network connectivity between MCP server and sensor devices"],"input_types":["motion-sensor-identifiers","subscription-filters","room-or-zone-names"],"output_types":["motion-event-stream","sensor-state-json","timestamp-and-location-metadata"],"categories":["tool-use-integration","home-automation","iot-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexanderlamb-domestic-motion__cap_1","uri":"capability://tool.use.integration.room.zone.motion.aggregation","name":"room-zone-motion-aggregation","description":"Aggregates motion events from multiple sensors across defined room or zone boundaries, providing agents with a unified view of occupancy and movement patterns at the room level rather than individual sensor level. Implements spatial grouping logic that correlates sensor readings to logical home zones, reducing noise and enabling higher-level reasoning about which areas are occupied.","intents":["Query which rooms currently have motion detected","Understand occupancy patterns across the home at a room granularity","Trigger zone-specific automations (e.g., lights on when motion in living room)","Reduce false positives by requiring motion consensus across multiple sensors in a zone"],"best_for":["Home automation builders who want room-level occupancy awareness","Developers building energy-efficient smart homes that respond to presence","Teams implementing multi-room automation workflows"],"limitations":["Requires pre-configured room/zone mappings — no automatic sensor-to-room discovery","Aggregation logic is static; does not adapt to changing home layouts or sensor relocations","No temporal smoothing or debouncing — rapid motion on/off transitions may create spurious zone state changes","Assumes sensors are already placed and calibrated for their assigned zones"],"requires":["Room/zone configuration file or schema defining sensor-to-room mappings","Multiple motion sensors per zone for meaningful aggregation","MCP server with zone-aware event routing logic"],"input_types":["motion-events-from-individual-sensors","room-zone-configuration","sensor-placement-metadata"],"output_types":["room-occupancy-state","zone-motion-summary","aggregated-event-stream"],"categories":["tool-use-integration","data-processing-analysis","home-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexanderlamb-domestic-motion__cap_2","uri":"capability://data.processing.analysis.motion.event.history.and.pattern.analysis","name":"motion-event-history-and-pattern-analysis","description":"Maintains a time-windowed history of motion events and exposes pattern analysis capabilities, allowing agents to query historical motion data and detect occupancy patterns (e.g., 'motion in kitchen between 7-9am daily'). Implements event buffering with configurable retention windows and provides statistical summaries of motion frequency, duration, and temporal clustering.","intents":["Analyze historical motion patterns to predict future occupancy","Detect anomalies (e.g., motion at unusual times indicating intrusion)","Build learning-based automations that adapt to household routines","Generate occupancy reports for energy optimization or security audits"],"best_for":["Developers building predictive home automation that learns from patterns","Security-focused builders implementing anomaly detection for intrusion alerts","Teams optimizing energy usage based on occupancy history"],"limitations":["History retention is time-windowed; older events are discarded (no long-term archival)","Pattern analysis is statistical only — no machine learning model training or forecasting","Requires sufficient historical data to detect meaningful patterns (cold-start problem)","No privacy-preserving aggregation — raw motion timestamps are retained"],"requires":["Configurable history retention window (e.g., 7 days, 30 days)","Time-series storage or in-memory buffer for event history","Statistical analysis library for pattern detection"],"input_types":["motion-event-stream","time-range-queries","pattern-detection-parameters"],"output_types":["historical-event-list","occupancy-statistics","pattern-summaries","anomaly-flags"],"categories":["data-processing-analysis","memory-knowledge","home-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexanderlamb-domestic-motion__cap_3","uri":"capability://tool.use.integration.sensor.configuration.and.metadata.exposure","name":"sensor-configuration-and-metadata-exposure","description":"Exposes motion sensor metadata (location, sensitivity, battery status, last-seen timestamp) and allows agents to query or update sensor configurations through MCP tools. Implements a configuration schema that maps sensor IDs to physical locations, sensor types, and operational parameters, enabling agents to understand sensor capabilities and health.","intents":["Query which motion sensors are available and their current status","Adjust sensor sensitivity or detection thresholds dynamically","Monitor sensor health (battery, connectivity) and alert on failures","Map sensor IDs to human-readable room names for agent reasoning"],"best_for":["Developers building self-aware smart home systems that understand their own sensor topology","Teams implementing sensor health monitoring and maintenance workflows","Builders creating adaptive automations that adjust sensor sensitivity based on context"],"limitations":["Configuration changes may not persist across server restarts without external storage","No validation of sensor configuration — agents can set invalid parameters","Sensor metadata is static; does not auto-discover new sensors or detect sensor relocation","Battery and connectivity status depends on sensor reporting; no independent health checks"],"requires":["Sensor configuration file or database (JSON, YAML, or database backend)","Sensor API or local integration that supports metadata queries","Schema definition for sensor types and configuration parameters"],"input_types":["sensor-id","configuration-parameters","metadata-queries"],"output_types":["sensor-metadata-json","configuration-schema","health-status","sensor-list"],"categories":["tool-use-integration","memory-knowledge","home-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alexanderlamb-domestic-motion__cap_4","uri":"capability://automation.workflow.motion.triggered.automation.orchestration","name":"motion-triggered-automation-orchestration","description":"Provides MCP tools for agents to define and trigger automations based on motion events, such as turning on lights, adjusting thermostats, or sending notifications. Implements a rule-action pattern where agents can register motion-triggered rules and the server executes corresponding actions, with support for conditional logic (e.g., 'turn on lights only if it's dark').","intents":["Automatically turn on lights when motion is detected in a room","Trigger multi-step automations (e.g., unlock door + turn on lights + adjust thermostat) on motion","Create conditional automations that consider time of day, occupancy, or other context","Cancel or modify automations dynamically based on agent reasoning"],"best_for":["Developers building LLM-driven home automation that responds to motion in real-time","Teams implementing complex multi-step automations triggered by occupancy","Builders creating context-aware smart homes that adapt behavior based on time and conditions"],"limitations":["Automation execution depends on integration with downstream smart home devices (lights, locks, thermostats)","No built-in error handling or rollback if an automation step fails","Conditional logic is limited to simple predicates; no complex state machines or temporal constraints","Automations are ephemeral — not persisted across server restarts unless explicitly saved"],"requires":["Integration with smart home control APIs (e.g., Home Assistant, SmartThings, Philips Hue)","Rule definition schema (JSON or similar) for motion-triggered actions","Action execution engine that can invoke downstream device APIs"],"input_types":["motion-event","automation-rule-definition","conditional-predicates","action-specifications"],"output_types":["automation-execution-status","action-results","error-logs"],"categories":["automation-workflow","tool-use-integration","home-automation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude desktop, LLM framework with MCP support)","Motion sensor hardware with network API or local integration","Network connectivity between MCP server and sensor devices","Room/zone configuration file or schema defining sensor-to-room mappings","Multiple motion sensors per zone for meaningful aggregation","MCP server with zone-aware event routing logic","Configurable history retention window (e.g., 7 days, 30 days)","Time-series storage or in-memory buffer for event history","Statistical analysis library for pattern detection","Sensor configuration file or database (JSON, YAML, or database backend)"],"failure_modes":["Requires compatible motion sensor hardware with network connectivity","Event latency depends on underlying sensor polling frequency and network conditions","No built-in event persistence or replay — events are ephemeral unless explicitly captured by the agent","Limited to motion detection; does not provide sensor calibration, sensitivity tuning, or multi-modal sensor fusion","Requires pre-configured room/zone mappings — no automatic sensor-to-room discovery","Aggregation logic is static; does not adapt to changing home layouts or sensor relocations","No temporal smoothing or debouncing — rapid motion on/off transitions may create spurious zone state changes","Assumes sensors are already placed and calibrated for their assigned zones","History retention is time-windowed; older events are discarded (no long-term archival)","Pattern analysis is statistical only — no machine learning model training or forecasting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:37.912Z","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=alexanderlamb-domestic-motion","compare_url":"https://unfragile.ai/compare?artifact=alexanderlamb-domestic-motion"}},"signature":"5xanb/ccSg4UV/LLeSfVmsVNlZf7Q6fDU2z10wYTj6qUvb2rSbA29gauuBTFoyivvDWZpdUh0HIv1X7BMfCyAA==","signedAt":"2026-06-21T17:10:51.902Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alexanderlamb-domestic-motion","artifact":"https://unfragile.ai/alexanderlamb-domestic-motion","verify":"https://unfragile.ai/api/v1/verify?slug=alexanderlamb-domestic-motion","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"}}