Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Jarzembak/calibremcp --skill semantic_searchgit clone --depth 1 https://github.com/Jarzembak/calibremcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jarzembak/calibremcp/semantic_search)<a href="https://agentmods.dev/skills/jarzembak/calibremcp/semantic_search"><img src="https://agentmods.dev/badge/skills/jarzembak/calibremcp/semantic_search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jarzembak/calibremcp/semantic_search"><img src="https://agentmods.dev/badge/skills/jarzembak/calibremcp/semantic_search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00599 |
| Opus 5 | $0.00000 | $0.00300 |
| Sonnet 5 | $0.00000 | $0.00120 |
| Haiku 4.5 | $0.00000 | $0.00060 |
Grade A, and why
semantic_search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to semantic_search — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Search (Metadata RAG)
Description: Find books by meaning using LanceDB over title, authors, tags, comments, and series. Supports hybrid keyword + vector search across your entire Calibre library.
Trigger Phrases
- "Find books about [topic]"
- "Search my library for [query]"
- "What do I have on [subject]?"
- "Show me books similar to [title]"
- "Find [author] books about [theme]"
Tools
calibre_metadata_index_build()— Build or rebuild the LanceDB metadata index. Run once per library or after large batch imports.calibre_metadata_search(query="...", top_k=10)— Natural-language semantic search over book metadata. Returns ranked results with relevance scores.rag_index_build()— Build full-text content index from ebook text (requires epub/mobi extraction).rag_retrieve(query="...", top_k=5)— Semantic search over full book contents, not just metadata.search_fulltext(query="...")— Legacy full-text search with exact phrase matching and boolean operators.query_books(search="...", tags=[...], authors=[...])— Structured metadata filtering with AND/OR logic.
Workflow
- Index check: If no index exists, call
calibre_metadata_index_build()first (runs async, takes 1-5 min per 1000 books). - Metadata search: Use
calibre_metadata_search()for broad semantic queries. Combine withtop_kto control result breadth. - Filter refinement: Narrow results by chaining with
query_books()using author, tag, or series filters. - Deep content search: For research-style queries, call
rag_retrieve()to search inside book text. Caveat: works best on epub format. - Result presentation: Return title, author, relevance score, and match highlights. Include calibre book_id for follow-up actions (open, metadata edit, export).
Search Operators
- Phrase match: Use quotes in
search_fulltext—"machine learning" - Boolean:
search_fulltext(query="python AND (data science OR ML)") - Tag filter:
query_books(tags=["python", "tutorial"], limit=20) - Date range:
query_books(added_since="2024-01-01")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 42 lines · 0 tokens per session scan A 9249bd9f8a2d
semantic_search is a skill published in the GitHub repository Jarzembak/calibremcp (0 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 599 tokens. A static security scan graded it A with 0 findings. It is 100% identical to semantic_search, differing in 0 lines, and is treated as a copy.
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