Apple Notes
MCP ServerFree** - Talk with your Apple Notes
Capabilities8 decomposed
hybrid semantic-keyword search over local apple notes
Medium confidenceCombines 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.
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
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
on-device embedding generation with all-minilm-l6-v2 transformer
Medium confidenceGenerates 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.
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
Avoids API latency and costs of cloud embedding services (OpenAI, Cohere) while maintaining semantic search capabilities, though with lower embedding dimensionality than enterprise alternatives
mcp protocol bridge for claude desktop integration
Medium confidenceImplements 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.
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
Provides native MCP integration with Claude Desktop rather than requiring browser extensions or API wrappers, enabling seamless tool invocation within Claude's native interface
javascript for automation (jxa) bridge to apple notes
Medium confidenceUses 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.
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
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
note indexing and vector database persistence
Medium confidenceOrchestrates 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.
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
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
note listing and metadata retrieval
Medium confidenceExposes 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.
Provides lightweight note listing via JXA that returns only metadata without full content retrieval, enabling efficient note discovery and selection before detailed content access
Separates note discovery from content retrieval, allowing users to browse available notes without loading full content into Claude's context window
targeted note retrieval by identifier
Medium confidenceRetrieves 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.
Implements direct note retrieval by identifier via JXA, bypassing search and vector operations for cases where specific note access is needed
Provides direct note access without semantic search overhead when note identifier is known, enabling fast targeted retrieval for specific notes
note creation from claude conversations
Medium confidenceEnables 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.
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
Enables Claude to persist insights and generated content directly to Apple Notes rather than requiring manual copy-paste or external note creation tools
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Individual knowledge workers maintaining personal note collections in Apple Notes
- ✓Developers building AI agents that need access to local, private knowledge bases
- ✓Users who want RAG capabilities without cloud data transmission
- ✓Privacy-conscious users who cannot send note content to external services
- ✓Developers building offline-first AI applications
- ✓Teams with air-gapped or restricted network environments
- ✓Claude Desktop users who want native note integration without plugins
- ✓Developers building MCP-compatible AI applications
Known Limitations
- ⚠Embedding generation uses all-MiniLM-L6-v2 model which may have lower semantic quality than larger models like OpenAI embeddings
- ⚠Search performance depends on LanceDB indexing speed; large note collections (10k+ notes) may have slower initial indexing
- ⚠Hybrid search requires tuning of vector/keyword result weighting; default algorithm may not optimize for all use cases
- ⚠No support for searching note metadata (creation date, tags) — only full-text and semantic content search
- ⚠all-MiniLM-L6-v2 produces 384-dimensional embeddings which may have lower semantic quality than larger models (OpenAI's text-embedding-3-large produces 3072 dimensions)
- ⚠Embedding generation adds latency during initial indexing; large note collections may require several minutes to process
Requirements
Input / Output
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