| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Bridges domestic motion sensors directly into MCP protocol, enabling LLM agents to subscribe to motion events as first-class resources rather than polling external APIs or webhooks, with native streaming semantics
vs alternatives: Provides tighter integration with LLM reasoning loops than REST-based sensor APIs because MCP's resource subscription model allows agents to maintain continuous awareness of motion state without explicit polling overhead
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.
Unique: Implements spatial aggregation at the MCP server layer, allowing agents to query room-level occupancy as a single resource rather than correlating multiple sensor events themselves, reducing agent-side complexity
vs alternatives: Simpler for agents than manually correlating sensor events because aggregation happens server-side; agents get clean room-level state without needing to maintain spatial reasoning logic
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.
Unique: Exposes motion history and pattern analysis as MCP resources, allowing agents to query historical occupancy without external database dependencies; patterns are computed server-side and served as structured data
vs alternatives: Agents can reason about historical patterns without building their own time-series storage or analysis logic; patterns are pre-computed and cached, reducing per-query latency vs. on-demand analysis
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.
Unique: Exposes sensor metadata and configuration as queryable MCP resources, allowing agents to introspect the sensor topology and adjust parameters without hardcoding sensor IDs or relying on external configuration files
vs alternatives: Agents can dynamically discover and configure sensors at runtime via MCP tools rather than requiring pre-deployment configuration; enables more flexible and self-aware automation systems
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').
Unique: Allows agents to define and execute motion-triggered automations through MCP tools, enabling dynamic rule creation at runtime rather than static configuration; agents can reason about conditions and adapt automations in real-time
vs alternatives: More flexible than static automation rules because agents can dynamically create, modify, and cancel automations based on reasoning; enables adaptive behavior that responds to changing context
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs domestic-motion at 22/100. domestic-motion leads on ecosystem, while Supabase is stronger on quality.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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