The Zotero Chunk RAG server enables semantic search and retrieval over a Zotero research library using AI embeddings and configurable reranking. Key capabilities include:
search_papers— Passage-level semantic search across indexed PDF chunks, returning matched text with surrounding context (0–3 adjacent chunks) and bibliographic metadata (authors, year, citation keys), with optional year filtering.search_topic— Find the most relevant papers (deduplicated by document) for a broad topic, scoring each by average and best chunk relevance, with optional year filtering and up to 50 results.get_passage_context— Expand the context window around a specific passage from a prior search, retrieving 1–5 surrounding chunks for deeper reading.search_tables— Find tables within indexed PDFs by content using natural language, returning results in markdown format with captions and bibliographic details.get_reranking_config— View current reranking settings (section weights, journal quartile weights, alpha exponent) to understand result ordering.get_index_stats— Check index status: total documents, total chunks, and average chunks per document.
Results are reranked using a composite score combining semantic similarity with document metadata (e.g., paper section like 'Results'/'Methods' and journal impact quartile). The system also supports OCR for scanned PDFs and customizable ranking weights.
Enables passage-level semantic search and retrieval across a Zotero library, allowing users to find relevant papers by topic, extract bibliographic metadata, and access specific text passages with surrounding context from PDF attachments.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Zotero Chunk RAGFind passages in my papers about transformer architecture."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.jsonEdit ~/.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 -vTo test with a subset first:
deep-zotero-index --limit 10 -vThis 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 |
| Delete and rebuild index for all matching items |
| Only index N items |
| Index a single Zotero item |
| Regex filter on title (case-insensitive) |
| Skip vision table extraction for this run |
| Use a different config file |
| 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 |
|
| Path to Zotero's data directory (contains |
|
| Where the ChromaDB index is stored on disk |
Embedding
Field | Default | Description |
|
|
|
|
| Gemini model name (only used when provider is |
|
| Output vector dimensions. |
|
| Falls back to |
|
| Timeout in seconds for embedding API calls |
|
| Max retries for failed embedding calls |
Chunking
Field | Default | Description |
|
| Target chunk size in tokens (~4 chars/token). Changing requires |
|
| Overlap between consecutive chunks in tokens |
Vision
Field | Default | Description |
|
| Enable vision table extraction during indexing |
|
| Anthropic model for table transcription |
|
| Falls back to |
Reranking
Field | Default | Description |
|
| Enable composite score reranking |
|
| Similarity exponent (0-1). Lower = more metadata influence |
|
| Override default section weights |
|
| Override default journal quartile weights |
|
| Oversample factor before reranking |
|
| Additional factor for |
|
| Max chunks sampled for |
OCR
Field | Default | Description |
|
| Tesseract language code for scanned pages ( |
OpenAlex
Field | Default | Description |
|
| Email for OpenAlex polite pool (10 req/s vs 1 req/s). Falls back to |
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_weightDefault 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 |
| string | Case-insensitive substring match against author names |
| string | Case-insensitive substring match against Zotero tags |
| string | Case-insensitive substring match against collection names |
| int | Publication year range |
| dict | Override section weights for this call |
| dict | Override journal quartile weights |
| list | Exact whole-word matches required in passage ( |
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.pyResources
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