psyXe MCP Server

psyXe MCP Server

An MCP server that gives AI assistants access to your Apple Notes, Reminders, and Contacts — with optional BERT-powered semantic search.

All tools run locally via macOS-native APIs (AppleScript, EventKit, Contacts framework). No data leaves your Mac. No API keys required.

New to MCP? Check out the FAQ for answers to common questions about what this is, whether it works with your setup, and how your data stays private.

Video Tutorials

What Can It Do?

Install

Homebrew (recommended)

brew tap bjenkinsgit/tapbrew install psyxe-mcp

This installs everything — binary, Swift helpers, FFmpeg, and the BERT model. No compilation required.

Build from Source

git clone https://github.com/bjenkinsgit/psyxe-mcp.gitcd psyxe-mcp./build.sh

The build script handles everything automatically:

• Installs Homebrew, Rust, FFmpeg, and pkg-config if missing
• Builds the MCP server binary (Rust)
• Builds Swift helpers for Reminders and Contacts
• Copies helpers next to the binary
• Pre-downloads the BERT model (~90MB) so first search is instant

Build without semantic search (skips FFmpeg and BERT):

./build.sh --no-memvid

Requirements: macOS 12+ (Monterey or later). Xcode Command Line Tools will be prompted if not installed.

The binary and helpers are in target/release/. Use the full path when configuring your MCP client.

Install Apple Shortcuts (optional)

Two shortcuts enable linking Reminders to file artifacts:

./install-shortcuts.sh

This opens each shortcut in Shortcuts.app for you to approve.

Configure Your MCP Client

If you installed via Homebrew, the command is just psyxe-mcp (it's in your PATH). If you built from source, use the full path: /Users/yourname/src/psyxe-mcp/target/release/psyxe-mcp.

Claude Code (CLI)

claude mcp add psyxe -- psyxe-mcp

Or edit ~/.claude/claude_mcp_config.json:

{  "mcpServers": {    "psyxe": {      "command": "psyxe-mcp"    }  }}

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{  "mcpServers": {    "psyxe": {      "command": "psyxe-mcp"    }  }}

Cursor

Open Settings → MCP Servers → Add new server:

{  "psyxe": {    "command": "psyxe-mcp"  }}

Windsurf

Edit ~/.codeium/windsurf/mcp_config.json:

{  "mcpServers": {    "psyxe": {      "command": "psyxe-mcp"    }  }}

OpenAI Codex CLI

Edit ~/.codex/config.toml:

[mcp_servers.psyxe]command = "psyxe-mcp"

Note: If you built from source instead of using Homebrew, replace psyxe-mcp with the full path to the binary (e.g., /Users/yourname/src/psyxe-mcp/target/release/psyxe-mcp).

Access Control

By default, the MCP server has full access to all your Notes, Reminders, Contacts, and files. To restrict what your AI can see, use the built-in access control CLI to create and manage ~/.psyxe/access.toml.

No manual file editing is needed — the CLI creates the file with secure permissions (owner-only read/write) on first use.

Quick Start

# 1. See what's availablepsyxe-mcp access discover reminderspsyxe-mcp access discover notes
# 2. Grant access to only what the AI should seepsyxe-mcp access grant reminders "Work"psyxe-mcp access grant notes "Projects"
# 3. Verify your restrictionspsyxe-mcp access list

Once any rule is set for a category, only explicitly granted resources are accessible — everything else in that category is denied.

Discover What's Available

# See your reminder listspsyxe-mcp access discover reminders
# See your contact groupspsyxe-mcp access discover contacts
# See your note folderspsyxe-mcp access discover notes
# See common file locationspsyxe-mcp access discover files

Grant / Revoke Access

# Only allow access to specific reminder listspsyxe-mcp access grant reminders "Work"psyxe-mcp access grant reminders "Shopping" --rw    # read-write
# Only allow access to a specific contact grouppsyxe-mcp access grant contacts "iCloud"
# Only allow access to specific note folderspsyxe-mcp access grant notes "Projects"
# Grant file access to a folderpsyxe-mcp access grant files "/Users/you/Documents" --rw
# Revoke accesspsyxe-mcp access revoke reminders "Shopping"
# See current restrictionspsyxe-mcp access list
# Remove all restrictions (restore full access)psyxe-mcp access reset

Access rules are stored in ~/.psyxe/access.toml with owner-only permissions (chmod 600). The server refuses to load the config if it is group- or world-readable, preventing other processes from tampering with access rights.

Semantic Search

When built with the memvid feature (enabled by default), the server includes BERT-powered semantic search for Apple Notes. This uses memvid-rs to encode your notes into a searchable vector index.

First Use

The first time you (or your AI assistant) run a semantic search, the server will build an index of all your notes. This takes a few minutes depending on how many notes you have. Subsequent searches are instant.

# Or ask your AI assistant: "search my notes for machine learning concepts"# It will automatically build the index on first use.

How It Works

1. All notes are fetched from Notes.app
2. Each note is chunked and encoded with a BERT model (384-dimensional embeddings)
3. Chunks are stored as QR codes in a ProRes video file (compact, durable archive)
4. A vector index (HNSW) enables instant semantic similarity search
5. The index auto-detects when notes change and prompts for rebuild

Try It Out

We've included sample notes designed to showcase semantic search:

# Load 10 sample notes into Apple Notes./examples/load-sample-notes.sh
# Then ask your AI assistant to rebuild the index and try queries like:#   "retirement savings"  → finds Tax Strategy (never mentions "retirement")#   "Italian cooking"     → finds Carbonara recipe (never says "Italian")

See examples/sample-notes.md for the full list of demo queries.

Choosing a Different BERT Model

The default model (sentence-transformers/all-MiniLM-L6-v2, 384 dimensions) balances speed and quality. You can swap in any HuggingFace BERT-family sentence-transformer model.

Via environment variable:

MEMVID_MODEL_NAME=BAAI/bge-small-en-v1.5 target/release/psyxe-mcp warmup

Via config file — create memvid_config.toml in the repo root or next to the binary:

[ml]model_name = "BAAI/bge-small-en-v1.5"

After changing models, rebuild the index (ask your AI assistant or run warmup again).

Popular alternatives:

For instruction-tuned models (like BGE), add query/document prefixes:

[ml]model_name = "BAAI/bge-small-en-v1.5"embedding_query_prefix = "Represent this sentence for searching relevant passages: "embedding_document_prefix = ""

Remote Embedding API

Use any OpenAI-compatible embedding endpoint instead of local BERT:

[ml]embedding_provider = "remote"

Then set the endpoint via environment variables:

export EMBEDDING_API_URL="http://localhost:11434/v1/embeddings"  # Ollamaexport EMBEDDING_API_MODEL="nomic-embed-text"

Works with OpenAI, Ollama, vLLM, LM Studio, or any OpenAI-compatible endpoint.

Without Semantic Search

Build without memvid to skip the FFmpeg/BERT dependency entirely (no brew install needed):

cargo build --release --no-default-features

Notes tools still work — they fall back to AppleScript-based text search. All other tools (Reminders, Contacts, Files) are unaffected.

macOS Permissions

On first use, macOS will prompt you to grant permission for:

• Notes — "osascript" wants to access Notes
• Reminders — "reminders-helper" wants to access Reminders
• Contacts — "contacts-helper" wants to access Contacts

Approve these in the dialog that appears. You can review/revoke them later in System Settings → Privacy & Security.

Architecture

┌─────────────────┐     stdio (JSON-RPC)     ┌──────────────┐│  Claude Code /   │ ◄─────────────────────► │  psyxe-mcp   ││  Claude Desktop  │                          │  (MCP server) │└─────────────────┘                          └──────┬───────┘                                                     │                                    ┌────────────────┼────────────────┐                                    ▼                ▼                ▼                             ┌────────────┐  ┌─────────────┐  ┌───────────┐                             │ AppleScript │  │ Swift Helper│  │  memvid   │                             │ (Notes)     │  │ (EventKit,  │  │ (BERT +   │                             │             │  │  Contacts)  │  │  ProRes)  │                             └──────┬──────┘  └──────┬──────┘  └─────┬─────┘                                    ▼                ▼               ▼                             ┌────────────┐  ┌─────────────┐  ┌───────────┐                             │  Notes.app  │  │   EventKit  │  │ NoteStore │                             │             │  │   Contacts  │  │  SQLite   │                             └────────────┘  └─────────────┘  └───────────┘

The MCP server is a thin stdio bridge. All the real work happens in psyxe-mcp-core, the open-source library that provides direct access to macOS-native APIs.

Configuration

Semantic Search (memvid)

Place a memvid_config.toml in the working directory or next to the binary:

[chunking]chunk_size = 700overlap = 100
[ml]device = "metal"    # auto | cpu | cuda | metal
[qr]error_correction = "low"version = 40
[video]codec = "prores_ks"prores_profile = "proxy"library_log_level = "error"

See memvid-rs for all configuration options.

Environment Variables

Troubleshooting

"osascript is not allowed to send keystrokes" Grant Accessibility permission: System Settings → Privacy & Security → Accessibility

"reminders-helper" wants to access your Reminders Click Allow. If you previously denied, re-enable in System Settings → Privacy & Security → Reminders.

Semantic search is slow on first run The BERT model downloads on first use (~90MB). Subsequent runs use the cached model. Index building speed depends on note count — Metal GPU acceleration helps significantly on Apple Silicon.

Notes search returns stale results The server monitors for changes and will prompt your AI assistant to rebuild the index. You can also force it: ask your assistant to "rebuild the notes index".

License

Apache 2.0 — see LICENSE.

This project uses FFmpeg at runtime for ProRes video encoding only (LGPL codec). No GPL-licensed codecs (x264, x265, etc.) are used.

Credits

Built on psyxe-mcp-core, powered by memvid-rs for semantic search.