| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Combines vector-based semantic search using all-MiniLM-L6-v2 embeddings stored in LanceDB with traditional full-text keyword matching to retrieve notes based on meaning and exact terms. The system generates embeddings on-device during indexing, stores them in a local vector database, and executes hybrid queries that merge both result sets for comprehensive retrieval without sending note content to external services.
Unique: Implements hybrid search combining LanceDB vector operations with keyword matching entirely on-device using all-MiniLM-L6-v2 embeddings, eliminating cloud dependencies while maintaining semantic search capabilities through local transformer inference
vs alternatives: Provides semantic search over private notes without external API calls or data transmission, unlike cloud-based RAG systems that require uploading content to third-party services
Generates vector embeddings for note content using the all-MiniLM-L6-v2 transformer model executed locally via JavaScript/Node.js runtime, storing 384-dimensional vectors in LanceDB without external API calls. The embedding function processes text during the indexing phase and enables semantic similarity comparisons for search queries without requiring API keys or cloud infrastructure.
Unique: Executes all-MiniLM-L6-v2 transformer inference entirely on-device within the Bun runtime, eliminating external API dependencies and ensuring note content never leaves the local machine during embedding generation
vs alternatives: Avoids API latency and costs of cloud embedding services (OpenAI, Cohere) while maintaining semantic search capabilities, though with lower embedding dimensionality than enterprise alternatives
Implements the Model Context Protocol (MCP) server specification to expose Apple Notes tools to Claude Desktop through a standardized tool-calling interface. The server registers tool definitions via ListToolsRequestSchema, handles tool invocations through CallToolRequestSchema, and manages bidirectional communication with Claude, enabling the AI assistant to invoke note operations as native MCP tools without custom integrations.
Unique: Implements MCP server specification to expose Apple Notes as native Claude Desktop tools, using ListToolsRequestSchema and CallToolRequestSchema handlers to provide standardized tool definitions and execution without custom Claude plugins
vs alternatives: Provides native MCP integration with Claude Desktop rather than requiring browser extensions or API wrappers, enabling seamless tool invocation within Claude's native interface
Uses macOS JavaScript for Automation (JXA) to directly interact with the Apple Notes application, enabling programmatic note retrieval, listing, and creation without relying on file system access or reverse-engineered APIs. The JXA integration handles native Apple Events to query the Notes database and create new notes while maintaining compatibility with Apple's official automation framework.
Unique: Leverages macOS JavaScript for Automation (JXA) to directly invoke Apple Events on the Notes application, providing native integration without file system parsing or reverse-engineered APIs
vs alternatives: Uses official Apple automation APIs (JXA) rather than parsing proprietary Notes database files, ensuring compatibility with future macOS versions and respecting Apple's intended automation patterns
Orchestrates the indexing workflow that retrieves all notes from Apple Notes via JXA, generates embeddings for each note using all-MiniLM-L6-v2, and persists the embeddings along with note metadata in a LanceDB vector database for efficient retrieval. The indexing process is one-time or periodic, storing vector representations and note references locally to enable fast semantic search without re-embedding on each query.
Unique: Implements a complete indexing pipeline that retrieves notes via JXA, generates embeddings on-device, and stores them in LanceDB with note metadata, enabling persistent vector search without external services
vs alternatives: Provides local vector database persistence using LanceDB rather than in-memory embeddings, enabling fast searches across large note collections without re-computing embeddings on each query
Exposes a tool that retrieves the complete list of available notes from Apple Notes via JXA, returning note titles, identifiers, and basic metadata without requiring full content retrieval. This enables Claude to browse available notes and select specific ones for detailed retrieval, supporting note discovery workflows without loading all note content into context.
Unique: Provides lightweight note listing via JXA that returns only metadata without full content retrieval, enabling efficient note discovery and selection before detailed content access
vs alternatives: Separates note discovery from content retrieval, allowing users to browse available notes without loading full content into Claude's context window
Retrieves the full content of a specific note by identifier from Apple Notes via JXA, enabling Claude to access detailed note content after discovery or search. The retrieval operation fetches the complete note text and metadata, making it available for Claude to reference, summarize, or use in reasoning without requiring re-indexing or vector search.
Unique: Implements direct note retrieval by identifier via JXA, bypassing search and vector operations for cases where specific note access is needed
vs alternatives: Provides direct note access without semantic search overhead when note identifier is known, enabling fast targeted retrieval for specific notes
Enables Claude to create new notes in Apple Notes directly from conversations by invoking a JXA-based tool that writes note content and title to the Notes application. The creation operation accepts title and content parameters from Claude, constructs a new note object, and persists it to Apple Notes without requiring manual user interaction or file system access.
Unique: Provides bidirectional integration where Claude can not only read notes but also create new notes in Apple Notes via JXA, enabling write-back workflows from conversations
vs alternatives: Enables Claude to persist insights and generated content directly to Apple Notes rather than requiring manual copy-paste or external note creation tools
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 Apple Notes at 21/100.
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