Skip to main content
Glama
ccam80
by ccam80

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

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.

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
Install Server
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ccam80/zotero-chunk-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server