Zotero Code Execution

Zotero Code Execution

> Efficient multi-strategy Zotero search using code execution pattern

[](https://opensource.org/licenses/MIT)

A Python library for Zotero MCP that implements Anthropic's code execution pattern to enable safe, comprehensive searches without context overflow or crashes.

Skill Installation

For Claude Code

1. Clone or download this repository
2. Copy the skill/ folder to your Claude Code skills directory:

``bash cp -r skill ~/.claude/skills/zotero-mcp-code ``

1. Restart Claude Code to load the skill

Quick Start

import sys
sys.path.append('/path/to/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results

# Single comprehensive search - fetches 100+ items, returns top 20
orchestrator = SearchOrchestrator()
results = orchestrator.comprehensive_search("embodied cognition", max_results=20)
print(format_results(results))

That's it! This automatically:

• ✅ Performs semantic + keyword + tag searches
• ✅ Deduplicates results
• ✅ Ranks by relevance
• ✅ Keeps large datasets in code (no crashes)

Multi-Term Searches

For OR-style searches (e.g., multiple spellings or languages), search each term separately and merge:

# Search for "Atayal" OR "泰雅族"
all_results = {}

for term in ['Atayal', '泰雅族']:
    results = orchestrator.comprehensive_search(term, max_results=50)
    for item in results:
        all_results[item.key] = item  # Deduplicate by key

# Re-rank combined results
ranked = orchestrator._rank_items(list(all_results.values()), 'Atayal 泰雅族')
print(format_results(ranked[:25]))

Why? Zotero treats multi-word queries as AND conditions. Searching "Atayal 泰雅族" finds items matching BOTH terms, not either term.

Why This Exists

The Problem

Direct MCP tool calls have limitations:

• 🚫 Crash risk with large result sets (>15-20 items)
• 🚫 Token bloat - all results load into LLM context
• 🚫 Manual orchestration - multiple searches, manual deduplication
• 🚫 No ranking - results not sorted by relevance

The Solution

Code execution keeps large datasets in the execution environment:

• ✅ No crashes - only filtered results return to context
• ✅ Token efficient - process 100+ items, return top 20
• ✅ Auto-orchestration - multi-strategy search in one call
• ✅ Auto-ranking - results sorted by relevance

Features

Multi-Strategy Search

One function call performs:

• Semantic search (multiple variations)
• Keyword search (multiple modes)
• Tag-based search
• Automatic deduplication
• Relevance ranking

Safe Large Searches

# ❌ Old way: Crash risk
results1 = zotero_semantic_search("query", limit=10)  # Limited to 10
results2 = zotero_search_items("query", limit=10)     # Another 10
# Manual deduplication, manual ranking...

# ✅ New way: Safe and comprehensive
orchestrator = SearchOrchestrator()
results = orchestrator.comprehensive_search("query", max_results=20)
# Fetches 100+, processes in code, returns top 20

Advanced Filtering

# Fetch broadly, filter in code
library = ZoteroLibrary()
items = library.search_items("machine learning", limit=100)  # Safe!

# Filter to recent journal articles
filtered = orchestrator.filter_by_criteria(
    items,
    item_types=["journalArticle"],
    date_range=(2020, 2025)
)

Installation

Requirements

• Python 3.8+
• Zotero MCP installed via pipx
• Claude Code or similar code execution environment

Setup

1. Clone this repository:

git clone https://github.com/yourusername/zotero-code-execution.git
cd zotero-code-execution

1. Install dependencies (optional - usually already installed with Zotero MCP):

pip install -r requirements.txt

1. Use in your code:

import sys
sys.path.append('/path/to/zotero-code-execution')
import setup_paths  # Adds zotero_mcp to path
from zotero_lib import SearchOrchestrator, format_results

Usage Examples

Basic Search

orchestrator = SearchOrchestrator()
results = orchestrator.comprehensive_search("neural networks", max_results=20)
print(format_results(results))

Filter by Author

library = ZoteroLibrary()
results = library.search_items("Kahneman", qmode="titleCreatorYear", limit=50)
sorted_results = sorted(results, key=lambda x: x.date, reverse=True)
print(format_results(sorted_results))

Tag-Based Search

library = ZoteroLibrary()
results = library.search_by_tag(["learning", "cognition"], limit=50)
print(format_results(results[:20]))

Recent Papers

library = ZoteroLibrary()
results = library.get_recent(limit=20)
print(format_results(results))

Custom Filtering

library = ZoteroLibrary()
orchestrator = SearchOrchestrator(library)

items = library.search_items("AI", limit=100)

# Only recent papers with DOI
recent_with_doi = [
    item for item in items
    if item.doi and item.date and int(item.date[:4]) >= 2020
]
print(format_results(recent_with_doi))

See [examples.py](examples.py) for 8 complete working examples.

Claude Code Skill

This repository includes a Claude Code skill for easy integration.

Installation

Copy the skill to your Claude skills directory:

cp -r claude-skill ~/.claude/skills/zotero-mcp-code

Usage

In Claude Code, searches will automatically use the code execution pattern:

> "Find papers about embodied cognition"

Claude will write code using this library instead of direct MCP calls.

See [claude-skill/SKILL.md](claude-skill/SKILL.md) for complete skill documentation.

API Reference

SearchOrchestrator

Main class for automated multi-strategy searching.

comprehensive_search(query, max_results=20, use_semantic=True, use_keyword=True, use_tags=True, search_limit_per_strategy=50)

Performs comprehensive search with automatic deduplication and ranking.

Returns: List of ZoteroItem objects

filter_by_criteria(items, item_types=None, date_range=None, required_tags=None, excluded_tags=None)

Filter items by various criteria.

Returns: Filtered list of ZoteroItem objects

ZoteroLibrary

Low-level interface to Zotero.

• search_items(query, ...) - Keyword search
• semantic_search(query, ...) - Semantic/vector search
• search_by_tag(tags, ...) - Tag-based search
• get_recent(limit) - Recently added items
• get_tags() - All library tags

Helper Functions

• format_results(items, include_abstracts=True, max_abstract_length=300) - Format as markdown

See [READMELIBRARY.md](READMELIBRARY.md) for complete API documentation.

Architecture

Based on Anthropic's code execution with MCP:

1. Claude writes Python code (not direct MCP calls)
2. Code fetches large datasets (100+ items) from Zotero
3. Code processes in execution environment (dedup, rank, filter)
4. Only filtered results return to LLM context (20 items)

Result: Large datasets stay out of context, preventing crashes and saving tokens.

Performance

Expected Benefits

Based on Anthropic's pattern and implementation design:

• Token reduction: 50-90% (exact amount depends on search size)
• Function calls: 5-10x → 1x (confirmed by design)
• Search limits: 10-15 → 100+ items (safe in code)
• Crash prevention: Likely effective (untested)

Status

⚠️ Proof of concept - Performance claims are theoretical projections, not measured results.

See [HONESTSTATUS.md](HONESTSTATUS.md) for detailed status and validation needs.

Documentation

• [READMELIBRARY.md](READMELIBRARY.md) - Complete library documentation
• [QUICKSTART.md](QUICKSTART.md) - Quick reference guide
• [CLAUDEINSTRUCTIONS.md](CLAUDEINSTRUCTIONS.md) - Instructions for Claude Code
• [examples.py](examples.py) - 8 working examples
• [IMPLEMENTATIONSUMMARY.md](IMPLEMENTATIONSUMMARY.md) - Technical details
• [HONESTSTATUS.md](HONESTSTATUS.md) - Implementation status
• [claude-skill/SKILL.md](claude-skill/SKILL.md) - Claude Code skill docs

Contributing

Contributions welcome! Areas for improvement:

1. Performance validation - Measure actual token savings
2. Better ranking - Incorporate semantic similarity scores
3. Caching - Cache search results with invalidation
4. Parallel processing - Execute search strategies concurrently
5. Export functions - Batch BibTeX generation, CSV export

License

MIT License - see [LICENSE](LICENSE) file for details.

Credits

• Based on Zotero MCP
• Inspired by Anthropic's code execution with MCP

Related Projects

• Zotero MCP - The underlying MCP server
• Claude Code - Code execution environment
• FastMCP - MCP server framework

Citation

If you use this in research, please cite:

@software{zotero_code_execution,
  title = {Zotero Code Execution: Efficient Multi-Strategy Search},
  year = {2025},
  url = {https://github.com/kerim/zotero-code-execution}
}

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kerim
• Source: kerim/zotero-code-execution
• License: MIT

Install and usage instructions live in the source repository linked above.