DeepZotero

Semantic search over a Zotero library. PDFs are extracted (text, tables, figures), chunked, embedded, and stored in ChromaDB. An MCP server exposes the index to Claude Code (or any MCP client) as 10 tools for semantic and exact-word search over text, tables and figures, context expansion, citation graph lookup, indexing, and cost tracking.

What it extracts

• Text — section-aware chunks with overlap, classified by document section (abstract, methods, results, etc.)

• Tables — vision-based extraction via Claude Haiku 4.5. Each table is rendered to PNG and transcribed to structured markdown (headers, rows, footnotes). Table extraction is vision-only: with vision disabled, or without an Anthropic key, no tables are extracted.

• Figures — detected with captions, extracted as PNGs, searchable by caption text.

Related MCP server: zotero-kb

Requirements

• Python 3.10+

• A Gemini API key for embeddings (unless using embedding_provider: "local")

• An Anthropic API key for vision-based table extraction (optional but recommended)

• Zotero 8 with PDFs in storage/. Citation keys are read from Zotero's native citationKey field, which earlier versions do not have — on Zotero 7 every citation key comes back empty.

• Tesseract-OCR — only needed to OCR scanned / image-only PDF pages. Install Tesseract with the language data you need, then set the TESSDATA_PREFIX environment variable to its tessdata directory (e.g. C:\Program Files\Tesseract-OCR\tessdata). PyMuPDF locates the OCR data via that variable; without it, scanned pages are skipped ("OCR disabled because Tesseract language data not found."). Text-based PDFs do not need Tesseract.

Install as a Claude Code plugin

Requires uv on PATH. The repo is its own marketplace:

/plugin marketplace add ccam80/deep-zotero
/plugin install deep-zotero

The server launches through uvx, which fetches the pinned deep-zotero wheel from PyPI and caches it.

In the environment Claude Code starts from, set DEEP_ZOTERO_DATA_DIR (the Zotero data directory holding zotero.sqlite and storage/), DEEP_ZOTERO_CHROMA_PATH (where the index lives), GEMINI_API_KEY (embeddings) and ANTHROPIC_API_KEY (vision table extraction during indexing).

Index the library once before searching: deep-zotero-index -v.

/deep-zotero:install walks a coding agent through the whole setup: variables, Tesseract, and the first index.

Setup

1. Configuration

The four environment variables above are the whole configuration. Every other setting has a sensible default.

To override more than those four, write a JSON config and point DEEP_ZOTERO_CONFIG at it (or pass --config PATH to the CLI):

{
    "zotero_data_dir": "~/Zotero",
    "chroma_db_path": "~/.local/share/deep-zotero/chroma",
    "gemini_api_key": "YOUR_GEMINI_KEY",
    "anthropic_api_key": "YOUR_ANTHROPIC_KEY"
}

A config file takes precedence over the environment wherever both supply a setting. See Configuration reference for every field.

2. API keys

Gemini (required for default embeddings): Get a key at aistudio.google.com/app/apikey. Set it as gemini_api_key in config or GEMINI_API_KEY env var. If you don't want to use Gemini, set "embedding_provider": "local" to use ChromaDB's built-in all-MiniLM-L6-v2 model (no API key needed, lower quality).

Anthropic (required for vision table extraction): Get a key at console.anthropic.com. Set it as anthropic_api_key in config or ANTHROPIC_API_KEY env var. Table extraction is vision-only — without this key, text and figures are still indexed but no tables are extracted. Vision extraction uses the Anthropic Batch API with Claude Haiku 4.5 — cost is roughly $0.016 per table, with prompt caching reducing cost on large batches.

To disable vision extraction entirely:

{
    "vision_enabled": false
}

3. Index your library

deep-zotero-index -v

To test with a subset first:

deep-zotero-index --limit 10 -v

This reads the Zotero SQLite database (read-only, safe while Zotero is open), extracts text/tables/figures from each PDF, chunks the text, embeds via Gemini, and stores everything in ChromaDB.

CLI options:

The indexer is incremental — it only processes items not already in the index. Use --force after changing chunk_size, embedding_dimensions, or ocr_language.

You can also trigger indexing from the MCP client via the index_library tool.

4. Check it works

Call get_index_stats from Claude Code. It should report the documents and chunks just indexed.

If the tools are missing, the server did not start: confirm uv is on PATH and that the environment variables are visible to Claude Code's process.

For scanned-page OCR, TESSDATA_PREFIX (see Requirements) must be set in that same environment.

Configuration reference

Zotero

Embedding

Chunking

Vision

Reranking

OCR

OpenAlex

MCP tools

Semantic search

search_papers — Passage-level semantic search. Returns matching text with surrounding context, reranked by composite score (similarity × section weight × journal weight). Supports required_terms for combining semantic search with exact word matching — each term must appear as a whole word in the passage.

Parameters: query (optional when required_terms is given), top_k (1-50), context_chunks (0-3), year_min, year_max, author, tag, collection, chunk_types (text/figure/table), sections, journal_quartiles, section_weights, journal_weights, required_terms (words that must appear as whole words), terms_operator (AND/OR).

search_topic — Paper-level topic search, deduplicated by document. Groups chunks by paper, scores by average and best composite relevance.

Parameters: query, num_papers (1-50), year_min, year_max, author, tag, collection, chunk_types, sections, journal_quartiles, section_weights, journal_weights.

Filtering

search_papers(query="baroreflex sensitivity", author="Olufsen", journal_quartiles=["Q1"])
search_papers(required_terms=["SDNN"], sections=["results"], year_min=1991, year_max=1995)

Valid sections: abstract, introduction, background, methods, results, discussion, conclusion, references, appendix, preamble, table, figure, unknown.

Valid journal_quartiles: Q1, Q2, Q3, Q4, and unknown for journals with no quartile.

Tables and figures

search_papers covers text, tables and figures. Every result names its chunk_type; narrow with chunk_types:

search_papers("impedance measurement", chunk_types=["table"])

Table results add table_index, caption, num_rows and num_cols, with the table markdown in passage. Figure results add figure_index, caption and image_path (the extracted PNG), with the caption in passage.

Table and figure chunks carry section values of table and figure rather than the section they appeared in. Both weigh 1.0 in reranking.

Exact word matching

required_terms lists words that must appear in the passage as whole words, case-insensitively — heart matches Heart but not hearth. terms_operator is AND (default) or OR.

Terms constrain the search itself, so a passage containing a rare acronym is found even when semantic similarity would not rank it:

search_papers("autonomic regulation", required_terms=["SDNN"])

Omit query to retrieve every matching passage in the index, unranked, with no embedding call:

search_papers(required_terms=["propranolol", "SDNN"], terms_operator="AND")

At least one of query or required_terms is required. section_weights and journal_weights affect ranking only and are ignored when query is omitted. No phrase search, no stemming.

Context expansion

get_passage_context — Expand context around a passage from search_papers. For table results, pass table_page and table_index to find body text citing the table.

Parameters: doc_id, chunk_index, window (1-5), table_page, table_index.

Citation graph (OpenAlex)

Requires the document to have a DOI in Zotero.

find_citing_papers — Papers that cite a given document. Parameters: doc_id, limit (1-100).

find_references — Papers a document cites. Parameters: doc_id, limit (1-100).

get_citation_count — Citation and reference counts. Parameters: doc_id.

Index management

index_library — Trigger indexing from the MCP client. Parameters: force_reindex, limit, item_key, title_pattern, no_vision.

get_index_stats — Document/chunk/table/figure counts, section coverage, journal coverage. Counts cover the entire collection. The result is cached in index_stats.sqlite next to the Chroma database and refreshed at the end of every indexing run; pass refresh: true to force a recount.

get_reranking_config — Current reranking weights and valid override values.

get_vision_costs — Vision API batch usage and cost summary. Parameters: last_n (recent entries to show).

Reranking

Search results are scored:

composite_score = similarity^alpha * section_weight * journal_weight

Default section weights:

Default journal weights: Q1=1.0, Q2=0.85, Q3=0.65, Q4=0.45.

Override per-call via section_weights and journal_weights parameters. Set a section to 0 to exclude it. Disable reranking entirely with "rerank_enabled": false.

Shared filter parameters

Research agent skill

examples/zotero-research/SKILL.md is a ready-made Claude Code skill that wraps these tools into a spawnable research agent — it takes a high-level research question, runs the appropriate searches, and returns consolidated findings with citation keys. Copy it into .claude/skills/ (or your global skills directory) to use it.

Development

Debug viewer

tools/debug_viewer.py is a PyQt6 browser for inspecting the ChromaDB index — view papers, tables (rendered markdown vs PDF), figures, and individual chunks.

.venv/Scripts/python.exe tools/debug_viewer.py

Tests

.venv/Scripts/python.exe -m pytest

Tests that make real Anthropic API calls are marked vision_api and excluded by default; run them with -m vision_api.

tests/stress_test_real_library.py is the end-to-end quality gate: it pulls 10 papers from the live Zotero library, runs the full extraction → index → search pipeline into a temp ChromaDB, and asserts on extraction and retrieval quality. It writes STRESS_TEST_REPORT.md and _stress_test_debug.db.

.venv/Scripts/python.exe tests/stress_test_real_library.py

--vision-only re-runs just the vision extraction against an existing _stress_test_debug.db, optionally narrowed to one paper with --paper KEY.

Releasing

python tools/release.py --bump patch (or an explicit X.Y.Z) rewrites the version in pyproject.toml and plugin/.claude-plugin/plugin.json, including the pinned package the plugin launches, then commits and tags. It refuses a dirty tree, a non-main branch, and an existing tag.

Pushing the tag runs .github/workflows/publish.yml, which re-checks that tag, both manifests and the pin agree before publishing to PyPI.