imsg-mcp 0.1.2

imsg-mcp — iMessage CLI (imsg) + MCP (imsg-mcp). Typer, agent schema. macOS.

imsg — fast, local iMessage for your terminal & MCP

Package brand: imsg-mcp · CLI/MCP: imsg / imsg-mcp · PyPI today: pip install mac-imsg (target PyPI: imsg-mcp).

Read, search, and send iMessage from your terminal — or expose it to Claude, Cursor, VS Code, or any MCP client. Everything runs locally on your Mac: no cloud, no login, no account. Reads open ~/Library/Messages/chat.db read-only; sends go through Messages.app.

macOS only. iMessage lives in Apple's local chat.db and Messages.app — there is no official API and no equivalent on Linux or Windows, so imsg only runs on macOS. See Requirements for the Full Disk Access setup every install needs.

Why another one? The hot path — decoding tens of thousands of attributedBody typedstream blobs — is written in Rust (PyO3 + rusqlite), so reads and searches over a large history are several times faster than the pure-Python equivalent that other Messages servers use. Pure Python still ships as a zero-dependency fallback, so it works even where the wheel doesn't.

Benchmark

Full-history search over a synthetic 50,000-message database (70% stored as attributedBody blobs), best-of-5, Apple Silicon (16 cores):

operation	pure-Python	Rust core	speedup
full-history search (decodes every message)	120.9 ms	35.7 ms	3.4×
batched blob decode (allocation-bound)	19.1 ms	19.6 ms	1.0×

Two honest caveats, stated up front:

• Raw blob decoding is ~a wash — it's bound by allocating result strings, not CPU, so native code doesn't help. The win is in search, where Rust decodes and filters across all cores and returns only the matches.
• It's 3.4×, not 16×, because the SQLite read and result marshalling are serial on both sides; only the decode+match parallelizes. The gap widens on larger histories.

Reproduce: python bench/benchmark.py. Bonus: this search also finds messages whose text lives in an attributedBody blob — which a plain text LIKE query (used by Python-only Messages servers) silently misses.

Install

imsg is macOS-only. Pick one:

# Homebrew
brew install ml-lubich/tap/imsg

# pip
pip install mac-imsg

# uv (recommended) — provisions Python + the prebuilt wheel
uv tool install mac-imsg

# from a clone, building the Rust core yourself
git clone https://github.com/ml-lubich/imsg.git && cd imsg
uv tool install .

The PyPI package is mac-imsg — imsg-mcp (the eventual target name) was already registered when this shipped. The installed commands are always imsg and imsg-mcp, regardless of package name.

Requirements

• macOS. Reads live at ~/Library/Messages/chat.db; sends go through Messages.app AppleScript. Neither exists on Linux or Windows, so there's no cross-platform build.
• Full Disk Access for whichever app runs imsg:
  1. System Settings → Privacy & Security → Full Disk Access.
  2. Add the app that will run it — your terminal (Terminal/iTerm/Ghostty) for the CLI, or your MCP client (Claude Desktop, Cursor, VS Code, Claude Code) for the server.
  3. Fully quit and reopen that app — the permission doesn't take effect until relaunch.
• Messages.app signed in and able to send a normal message (only needed for imsg send).

Run imsg doctor to check access and see which engine (Rust or Python) is live.

Specs live under docs/ (overview, architecture, design, API, testing).

CLI

imsg -h                           # commands (also: imsg <cmd> -h)
imsg doctor                       # check Full Disk Access + engine
imsg chats                        # recent conversations + their ids
imsg contacts                     # handles (numbers / emails) seen
imsg contacts -q 415              # filter handles by substring
imsg read -c [phone]         # recent messages with a contact
imsg read --chat 42 --limit 100   # a specific conversation
imsg search "dinner"              # search message text
imsg send [phone] "on my way"
imsg version                      # installed package version
imsg agent schema                 # JSON schema of every stable command
imsg agent guide                  # markdown playbook for LLM agents

Every command accepts -h / --help with options, arguments, and examples (agent-friendly); imsg help <command> prints the same thing.

MCP server

The imsg-mcp entry point speaks MCP over stdio. Add it to any client:

Claude Code

claude mcp add --transport stdio --scope user imsg -- imsg-mcp

Claude Desktop / Cursor (mcpServers) · VS Code (servers):

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

Tools exposed: check_access, get_recent_messages, search_messages, list_chats, list_contacts, send_message (the only one with a side effect).

How it works

There is no official iMessage API. Every tool in this space does the same two local things; imsg just does the heavy half in Rust:

Operation	Mechanism	Engine
read / search / list	chat.db (SQLite, read-only) + typedstream decode	Rust (imsgcore), Python fallback
send	AppleScript → Messages.app	Python (osascript)

SQLite is the same C library everywhere, so the read speedup comes from doing the per-message attributedBody decode and row marshalling natively instead of in a Python loop. See bench/benchmark.py for the methodology — both engines run identical queries over an identical synthetic database, and the pure-Python column is the same algorithm Python-only servers use.

Privacy & security

• All database connections are opened read-only (mode=ro).
• Nothing is uploaded, mirrored, or indexed off-device.
• Sending is isolated in one function, escapes its AppleScript inputs, and is the only operation that writes anything anywhere.
• Full Disk Access is broad — grant it only to apps you trust.

Development

git clone https://github.com/ml-lubich/imsg.git && cd imsg
uv venv && source .venv/bin/activate
uv pip install maturin
maturin develop            # builds the Rust core + installs the package
uv pip install -e ".[dev]"
pytest                     # tests run on the pure-Python path (and Rust if built)
python bench/benchmark.py  # regenerate the benchmark

License

MIT © ml-lubich. Not affiliated with Apple. Use responsibly and only with accounts and conversations you own.