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 13 tools for semantic search, boolean search, table/figure search, 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). Falls back to PyMuPDF heuristics if vision is disabled.
• Figures — detected with captions, extracted as PNGs, searchable by caption text.

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)
• A Zotero installation with PDFs in storage/

Install

python -m venv .venv
.venv/Scripts/python.exe -m pip install -e .

For vision table extraction:

.venv/Scripts/python.exe -m pip install -e ".[vision]"

Setup

1. Configuration

mkdir -p ~/.config/deep-zotero
cp config.example.json ~/.config/deep-zotero/config.json

Edit ~/.config/deep-zotero/config.json:

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

All other fields have sensible defaults. You can also set GEMINI_API_KEY and ANTHROPIC_API_KEY as environment variables instead.

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. Without this key, tables are still extracted via PyMuPDF heuristics but accuracy on complex tables is lower. 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:

Flag	Description
--force	Delete and rebuild index for all matching items
--limit N	Only index N items
--item-key KEY	Index a single Zotero item
--title PATTERN	Regex filter on title (case-insensitive)
--no-vision	Skip vision table extraction for this run
--config PATH	Use a different config file
-v	Debug logging

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. Register the MCP server

Add to your Claude Code settings (~/.claude/settings.json):

{
    "mcpServers": {
        "deep-zotero": {
            "command": "/path/to/.venv/bin/python",
            "args": ["-m", "deep_zotero.server"]
        }
    }
}

On Windows:

{
    "mcpServers": {
        "deep-zotero": {
            "command": "C:\\path\\to\\.venv\\Scripts\\python.exe",
            "args": ["-m", "deep_zotero.server"]
        }
    }
}

Restart Claude Code. All 13 tools will be available.

---

Configuration reference

Zotero

Field	Default	Description
zotero_data_dir	~/Zotero	Path to Zotero's data directory (contains zotero.sqlite and storage/)
chroma_db_path	~/.local/share/deep-zotero/chroma	Where the ChromaDB index is stored on disk

Embedding

Field	Default	Description
embedding_provider	"gemini"	"gemini" for Gemini API, "local" for ChromaDB's built-in all-MiniLM-L6-v2 (no key needed)
embedding_model	"gemini-embedding-001"	Gemini model name (only used when provider is "gemini")
embedding_dimensions	768	Output vector dimensions. gemini-embedding-001 supports 64-3072. Changing requires --force re-index
gemini_api_key	null	Falls back to GEMINI_API_KEY env var
embedding_timeout	120.0	Timeout in seconds for embedding API calls
embedding_max_retries	3	Max retries for failed embedding calls

Chunking

Field	Default	Description
chunk_size	400	Target chunk size in tokens (~4 chars/token). Changing requires --force re-index
chunk_overlap	100	Overlap between consecutive chunks in tokens

Vision

Field	Default	Description
vision_enabled	true	Enable vision table extraction during indexing
vision_model	"claude-haiku-4-5-20251001"	Anthropic model for table transcription
anthropic_api_key	null	Falls back to ANTHROPIC_API_KEY env var

Reranking

Field	Default	Description
rerank_enabled	true	Enable composite score reranking
rerank_alpha	0.7	Similarity exponent (0-1). Lower = more metadata influence
rerank_section_weights	null	Override default section weights
rerank_journal_weights	null	Override default journal quartile weights
oversample_multiplier	3	Oversample factor before reranking
oversample_topic_factor	5	Additional factor for search_topic
stats_sample_limit	10000	Max chunks sampled for get_index_stats

OCR

Field	Default	Description
ocr_language	"eng"	Tesseract language code for scanned pages ("fra", "deu", etc.). Changing requires --force re-index

OpenAlex

Field	Default	Description
openalex_email	null	Email for OpenAlex polite pool (10 req/s vs 1 req/s). Falls back to OPENALEX_EMAIL env var

---

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, top_k (1-50), context_chunks (0-3), year_min, year_max, author, tag, collection, chunk_types (text/figure/table), section_weights, journal_weights, required_terms (list of words that must appear in passage).

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, section_weights, journal_weights.

search_tables — Semantic search over table content (headers, cells, captions). Returns tables as markdown.

Parameters: query, top_k (1-30), year_min, year_max, author, tag, collection, journal_weights.

search_figures — Semantic search over figure captions. Returns figure metadata and paths to extracted PNGs.

Parameters: query, top_k (1-30), year_min, year_max, author, tag, collection.

Boolean search

search_boolean — Exact word matching via Zotero's native full-text index. Returns papers (not passages) matching AND/OR word queries. No phrase search, no stemming.

Parameters: query (space-separated terms), operator (AND/OR), year_min, year_max.

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.

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:

Section	Weight
results	1.0
conclusion	1.0
table	0.9
methods	0.85
abstract	0.75
background	0.7
unknown	0.7
discussion	0.65
introduction	0.5
preamble	0.3
appendix	0.3
references	0.1

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

Parameter	Type	Description
author	string	Case-insensitive substring match against author names
tag	string	Case-insensitive substring match against Zotero tags
collection	string	Case-insensitive substring match against collection names
year_min / year_max	int	Publication year range
section_weights	dict	Override section weights for this call
journal_weights	dict	Override journal quartile weights
required_terms	list	Exact whole-word matches required in passage (search_papers only)

---

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