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MCP Apple Notes
An MCP service for semantic search of Apple Notes, supporting local embedding models, full-text search, and vector storage.
rating : 2.5 points
downloads : 12
What is MCP Apple Notes?
MCP Apple Notes is an application that enables semantic search and full-text retrieval through Apple Notes. It utilizes the on-device embedding model and LanceDB for vector storage to help you quickly find the information you need.How to use MCP Apple Notes?
First, configure the MCP server in Cursor IDE, and then use the provided tools to manage your Apple Notes, such as listing notes, indexing notes, and searching for notes.Applicable scenarios
Suitable for users who need to efficiently manage and retrieve the content of Apple Notes, such as researchers, writers, or knowledge workers.Main features
Semantic searchUtilize the embedding model to conduct in-depth analysis of note content, achieving a more accurate search experience.
Full-text retrievalSupport the traditional keyword-based search method to quickly locate specific notes.
Vector storageUse LanceDB to store note vectors and optimize search efficiency.
Native Apple Notes integrationSeamlessly connect to Apple Notes without the need for additional data import.
Advantages and limitations
Advantages
Efficiently retrieve important content in Apple Notes
Support semantic understanding and traditional keyword search
No network dependency, runs completely locally
Simple and easy to use, suitable for all types of users
Limitations
Only available for macOS users
Require the installation of specific environments (Bun and Git)
The initial indexing may take a long time
How to use
Install dependencies
Clone the repository and install the necessary dependencies.
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Configure Cursor IDE
Add the MCP server configuration in Cursor IDE.
Index notes
Run the indexing command to start searching.
Usage examples
List all notesView the titles of all currently stored notes.
Search for specific notesFind notes containing specific keywords.
Frequently Asked Questions
Does MCP Apple Notes support the Windows system?
How to solve the problem of slow initial indexing?
Related resources
Official documentation
Detailed installation guide and usage instructions.
GitHub repository
Source code address and contribution guide.
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