Booklore Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Booklore Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
An MCP server for BookLore. It lets any MCP-compatible assistant talk to your BookLore library: recommend books you already own, organize them into shelves, and send a book to your Kindle, all through BookLore's own REST API.
Because it speaks the open Model Context Protocol, it works with any MCP client, for example Claude Code, OpenAI Codex CLI, Cursor, Zed, and Windsurf. It runs over stdio and has no UI of its own.
| Tool | Purpose |
|---|---|
list_genres | List the genres/categories in your library with book counts |
search_books | Search by genre, author, title, free text, minimum rating, or unread only |
get_book | Full detail for one book (description, ratings, shelves) |
list_shelves | List all shelves |
create_shelf | Create a shelf (deduplicates by name) |
add_books_to_shelf | Add books to a shelf by id or name (can auto-create) |
list_email_recipients | List configured email recipients (for example Kindle addresses) |
send_to_kindle | Send a book using BookLore's native send-to-Kindle email feature |
Typical requests, in natural language: "recommend a well rated thriller I have not read", "create a shelf called Summer and add these five", "send Shogun to my Kindle".
In many libraries metadata.rating is empty and the real score lives in goodreadsRating or amazonRating. This server computes an effective rating (rating, then Goodreads, then Amazon) so that sorting and min_rating actually work.
send_to_kindle: an email provider (SMTP) and at least one recipient configuredin BookLore (Settings, Email), and the sender address approved in Amazon's Approved Personal Document E-mail List
git clone https://github.com/Giorgiofox/booklore-MCP.git
cd booklore-MCP
# install dependencies into a local ./libs folder (works without a virtualenv)
pip install --target ./libs -r requirements.txt
cp .env.example .env # then edit it
chmod 600 .envFill in .env:
BOOKLORE_URL=http://localhost:6060
BOOKLORE_USERNAME=your_user
BOOKLORE_PASSWORD=your_password
# set to false only if the TLS certificate is not valid on this host
BOOKLORE_VERIFY_SSL=truerun.sh loads .env, sets PYTHONPATH to ./libs, and starts the server over stdio.
Point your client at run.sh.
Claude Code, Cursor, and similar (JSON):
{
"mcpServers": {
"booklore": {
"command": "/absolute/path/to/booklore-MCP/run.sh"
}
}
}OpenAI Codex CLI (~/.codex/config.toml):
[mcp_servers.booklore]
command = "/absolute/path/to/booklore-MCP/run.sh"Restart the client and approve the booklore server when prompted.
It logs in with POST /api/v1/auth/login to obtain a JWT access token and refreshes it automatically. Credentials stay local in .env, which is git-ignored and never committed.
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.