| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to control various Home Assistant devices such as lights, climate, and locks using natural language commands. It employs a natural language processing (NLP) engine that interprets user input and maps it to specific device actions through a context-aware command parser, enabling seamless integration and execution of commands across multiple devices. This architecture supports dynamic command recognition and context management, differentiating it from simpler command-based systems.
Unique: Utilizes a context-aware NLP engine that can interpret and execute commands in real-time, adapting to user preferences and device states.
vs alternatives: More flexible than traditional command systems, allowing for conversational interactions rather than rigid command structures.
This capability enables users to trigger automations based on specific conditions or commands provided in natural language. It leverages a rule-based engine that evaluates user input against pre-defined automation rules, allowing for dynamic responses to various scenarios such as time-based triggers or device status changes. This approach allows for a high degree of customization and user control over home automation workflows.
Unique: Incorporates a rule-based engine that allows for natural language input to dynamically trigger complex automations, enhancing user engagement.
vs alternatives: More user-friendly than traditional automation setups, allowing for easy creation and modification of routines through simple commands.
This capability allows users to discover connected devices and check their status through natural language queries. It employs a device registry that maintains an updated list of all connected devices and their current states, enabling users to ask questions like 'What lights are on?' or 'Is the thermostat set to cool?'. This architecture ensures that users receive real-time information about their home environment.
Unique: Integrates a real-time device registry that allows for immediate status queries and updates, enhancing user awareness of their home environment.
vs alternatives: Provides more immediate and conversational access to device statuses compared to traditional dashboard interfaces.
This capability provides users with insights into energy consumption and device health through natural language queries. It utilizes data analytics to assess energy usage patterns and device performance metrics, allowing users to ask questions like 'How much energy did my lights use last week?' or 'Is my HVAC system functioning properly?'. This feature empowers users to make informed decisions about their home energy management.
Unique: Combines natural language processing with data analytics to provide actionable insights into energy use and device health, making it more accessible to non-technical users.
vs alternatives: Offers a more intuitive interface for energy management compared to traditional energy monitoring tools that require manual data interpretation.
This capability allows users to sync their smart lights with music playback, creating dynamic lighting effects that respond to audio. It utilizes audio analysis algorithms to detect beats and rhythms, which are then translated into corresponding light patterns through the Home Assistant's lighting control interface. This feature enhances the entertainment experience by providing a visually engaging atmosphere that matches the audio.
Unique: Employs real-time audio analysis to create responsive lighting effects, setting it apart from static lighting control systems.
vs alternatives: More dynamic and engaging than traditional lighting controls, providing an immersive experience that enhances music enjoyment.
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 advanced-homeassistant-mcp at 29/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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
+1 more capabilities