| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Home Assistant device control through MCP tools (entity_action, call_service_tool) that translate natural language requests into structured Home Assistant service calls. The FastMCP server acts as a protocol bridge, accepting tool invocations from LLM clients and routing them to Home Assistant's REST API with proper authentication via long-lived access tokens. Supports arbitrary service calls across all Home Assistant domains (lights, climate, switches, etc.) with parameter validation and response formatting optimized for token efficiency.
Unique: Implements MCP as a first-class protocol bridge to Home Assistant rather than wrapping REST APIs directly, enabling standardized LLM client integration (Claude, etc.) with schema-based service discovery and automatic parameter validation through Home Assistant's native service registry
vs alternatives: Provides tighter LLM integration than direct Home Assistant REST API calls because MCP's tool schema enables Claude and other clients to understand available services without custom prompt engineering, while maintaining Home Assistant as the authoritative device state store
Implements entity discovery through MCP tools (list_entities, get_entity, entity search) that query Home Assistant's entity registry and state store, returning filtered results based on domain, name, or semantic matching. The search capability uses natural language processing to map user queries (e.g., 'all lights in the bedroom') to entity filters, leveraging Home Assistant's entity metadata (friendly_name, domain, attributes) for intelligent matching. Results are formatted as structured data with current state, attributes, and available actions to minimize token usage in LLM context.
Unique: Bridges Home Assistant's entity registry with semantic search by exposing entity metadata through MCP resources (hass://entities/{entity_id}, hass://search/{query}) that LLM clients can query without parsing raw API responses, enabling natural language discovery without custom prompt engineering
vs alternatives: More discoverable than raw Home Assistant REST API because MCP resources provide structured entity metadata that LLM clients understand natively, while maintaining Home Assistant's entity registry as the single source of truth for device configuration
Provides MCP prompts (create_automation, debug_automation, troubleshoot_entity) that guide LLM clients through multi-step automation workflows using Home Assistant's automation framework. The system exposes automation listing, state queries, and service call capabilities that enable LLMs to both read existing automations and construct new ones by composing triggers, conditions, and actions. Guided prompts structure the conversation to elicit necessary parameters (trigger type, condition logic, action service calls) and validate them against Home Assistant's automation schema before execution.
Unique: Uses MCP prompts as structured conversation guides that decompose automation creation into multi-turn workflows, enabling LLMs to gather requirements and validate against Home Assistant's automation schema incrementally rather than requiring full automation specs upfront
vs alternatives: More accessible than direct YAML editing because MCP prompts guide users through required parameters step-by-step, while maintaining full compatibility with Home Assistant's native automation engine and allowing advanced users to edit generated YAML directly
Exposes Home Assistant's history API through MCP tools and resources to retrieve historical state data for entities, enabling trend analysis, pattern detection, and usage optimization suggestions. The system queries Home Assistant's state history database (typically SQLite or external database) to return time-series data for sensors, switches, and other stateful entities. LLM clients can analyze this data to identify patterns (peak usage times, recurring failures, anomalies) and suggest automations or optimizations based on observed behavior.
Unique: Integrates Home Assistant's state history database with LLM reasoning through MCP resources, enabling natural language queries over time-series data (e.g., 'show me when the living room light was on yesterday') without requiring users to understand SQL or Home Assistant's history schema
vs alternatives: Provides more accessible historical analysis than direct Home Assistant REST API because MCP clients can request trends in natural language and receive LLM-synthesized insights, while maintaining Home Assistant's native history storage and retention policies
Exposes Home Assistant system logs and error diagnostics through MCP tools (debug_automation, troubleshoot_entity) that enable LLM clients to diagnose why devices or automations are not functioning. The system retrieves recent error logs, automation trigger history, and entity state change logs from Home Assistant, correlating them to identify root causes (missing integrations, authentication failures, service timeouts, etc.). Troubleshooting prompts guide users through diagnostic workflows by asking targeted questions and analyzing logs to narrow down failure modes.
Unique: Combines Home Assistant's error logs with LLM reasoning through MCP prompts to provide guided troubleshooting workflows that correlate log entries with entity state changes and automation triggers, enabling root cause analysis without requiring users to parse raw logs manually
vs alternatives: More actionable than raw Home Assistant logs because MCP prompts guide users through diagnostic questions and synthesize log data into specific remediation steps, while maintaining access to Home Assistant's native logging and error reporting
Implements a FastMCP server that exposes Home Assistant capabilities through standardized MCP protocol interfaces (tools, resources, prompts), enabling multiple LLM clients (Claude, ChatGPT, custom agents) to connect via a single configuration. The server uses environment-based configuration (HA_URL, HA_TOKEN) to manage Home Assistant connectivity and exposes a consistent tool/resource/prompt schema that clients discover via MCP handshake. This abstraction decouples client implementations from Home Assistant API details, allowing clients to interact through high-level semantic operations.
Unique: Implements MCP as a protocol-level abstraction over Home Assistant's REST API, enabling clients to discover and invoke capabilities through standardized tool/resource/prompt schemas rather than learning Home Assistant's API directly, with FastMCP handling protocol negotiation and request routing
vs alternatives: More scalable than embedding Home Assistant integration code in each LLM client because a single MCP server serves multiple clients, while providing better client interoperability than direct REST API integration because MCP's schema-based discovery enables clients to understand available operations without custom prompt engineering
Generates high-level summaries of entity groups by domain (all lights, all sensors, all climate devices) through MCP resources and tools that aggregate entity state and metadata. The system queries Home Assistant's entity registry to group entities by domain, retrieves current state for each entity, and formats results as structured summaries that LLM clients can use for context-aware decision making. Summaries include entity counts, state distributions (e.g., 3 lights on, 2 lights off), and available actions per domain to minimize token usage in LLM context windows.
Unique: Aggregates Home Assistant entities by domain and generates structured summaries that LLM clients can use for context without parsing individual entity states, reducing token usage and enabling faster decision-making compared to listing all entities
vs alternatives: More efficient than querying individual entities because domain summaries provide aggregated state in a single request, while maintaining Home Assistant's entity registry as the source of truth and enabling clients to drill down into specific entities when needed
Implements secure authentication to Home Assistant using long-lived access tokens passed via HA_TOKEN environment variable, which are included in HTTP Authorization headers for all API requests. The system uses standard HTTP Bearer token authentication (Authorization: Bearer {token}) to authenticate requests to Home Assistant's REST API endpoints. Token management is delegated to Home Assistant's user interface — the MCP server does not generate or rotate tokens, only consumes them from environment configuration.
Unique: Delegates token management to Home Assistant's native user interface rather than implementing custom token generation, enabling users to revoke or rotate tokens through familiar Home Assistant settings without modifying MCP server configuration
vs alternatives: More secure than embedding credentials in configuration files because tokens are stored in environment variables and can be rotated independently, while maintaining Home Assistant's native authentication model and audit logging capabilities
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 Home Assistant at 25/100.
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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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