shrike-mcp

# Shrike Sometimes you want to work on your Anki collection without opening Anki. That's what Shrike is for. It's a command-line client and an MCP server for your collection, so you can manage cards from a terminal or hand them to an LLM agent. Shrike works directly on the collection's SQLite database, without using the Anki desktop app or relying on the AnkiConnect bridge. You get: - a `shrike` CLI to browse, create, edit, and delete notes and note types - an MCP server exposing the same operations to agents - semantic search over your notes, with embeddings computed locally ## Requirements - Python 3.12 or newer - An Anki collection (a `collection.anki2` file) ## Getting started Install Shrike from PyPI: ```bash pipx install shrike-mcp # or: pip install shrike-mcp ``` The package is `shrike-mcp`; the command it installs is `shrike`. Point Shrike at your collection and start the daemon: ```bash shrike server start --collection ~/path/to/collection.anki2 ``` That starts a background daemon with your collection open. The other `shrike` commands talk to it, so they don't repeat `--collection`: ```bash shrike info shrike note list --deck Default ``` And when you're done: ```bash shrike server stop ``` ## Semantic search `shrike note search` finds notes by meaning instead of keywords. It needs two things you supply yourself: a `llama-server` binary (from llama.cpp) to compute embeddings, and a GGUF embedding model for it to run. Get llama.cpp by building it or installing it from a package manager; you want the `llama-server` binary it provides. For the model, any GGUF embedding model works. A small one like [all-MiniLM-L6-v2](https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF) is a good default: it runs on CPU and is plenty for finding related cards. Start Shrike with both: ```bash shrike server start --collection ~/path/to/collection.anki2 \ --llama-server ~/llama.cpp/build/bin/llama-server \ --embedding-model ~/models/all-Mi…