Zotero MCP Code Execution Skill
Search your Zotero library using code execution for safe, efficient, comprehensive searches.
🎯 Core Concept
Instead of calling MCP tools directly (which loads all results into context and risks crashes), write Python code that:
- •Fetches large datasets (50-100+ items per strategy)
- •Filters and ranks in code execution environment
- •Returns only top N results to context
Benefits:
- •✅ No crash risk (large data stays in code)
- •✅ Automatic multi-strategy search
- •✅ Automatic deduplication
- •✅ Automatic ranking
- •✅ One function call instead of 5-10
⚠️ Critical Notes
Multi-Term Searches
Zotero treats multi-word queries as AND conditions!
❌ Wrong: comprehensive_search("Atyal Atayal 泰雅族") → finds 0 results (needs ALL terms)
✅ Right: Search each term separately and merge results (see Pattern 6 below)
When to use multi-term OR search:
- •Multiple spellings (Atayal, Atyal)
- •Multiple languages (Atayal, 泰雅族)
- •Synonyms (skill transfer, transfer of learning)
Semantic Search Status
Semantic search is now working correctly (fixed 2025-01-08). However, you may see 404 error messages in output from the semantic search engine itself - these can be ignored as our wrapper handles them correctly.
🚀 Basic Usage
For 90% of Zotero searches, use this simple pattern:
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results
# Single comprehensive search
orchestrator = SearchOrchestrator()
results = orchestrator.comprehensive_search(
"user's query here",
max_results=20, # Return top 20 most relevant
use_semantic=True # Semantic search now works correctly
)
# Format and display
print(format_results(results, include_abstracts=True))
This automatically:
- •Performs semantic search (vector similarity)
- •Performs keyword search (qmode="everything" + "titleCreatorYear")
- •Performs tag-based search
- •Fetches 100+ items total
- •Deduplicates results
- •Ranks by relevance
- •Returns only top 20 to context
📋 Common Patterns
Pattern 1: Simple Search (Most Common)
User asks: "Find papers about embodied cognition"
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results
orchestrator = SearchOrchestrator()
results = orchestrator.comprehensive_search("embodied cognition", max_results=20)
print(format_results(results))
Pattern 2: Filtered Search
User asks: "Find recent journal articles about machine learning"
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, SearchOrchestrator, format_results
library = ZoteroLibrary()
orchestrator = SearchOrchestrator(library)
# Fetch broadly (safe - filtering happens in code)
items = library.search_items("machine learning", limit=100)
# Filter in code
filtered = orchestrator.filter_by_criteria(
items,
item_types=["journalArticle"],
date_range=(2020, 2025)
)
print(format_results(filtered[:15]))
Pattern 3: Author Search
User asks: "What papers do I have by Kahneman?"
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, format_results
library = ZoteroLibrary()
results = library.search_items(
"Kahneman",
qmode="titleCreatorYear",
limit=50
)
# Sort by date
sorted_results = sorted(results, key=lambda x: x.date, reverse=True)
print(format_results(sorted_results))
Pattern 4: Tag-Based Search
User asks: "Show me papers tagged with 'learning' and 'cognition'"
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, format_results
library = ZoteroLibrary()
results = library.search_by_tag(["learning", "cognition"], limit=50)
print(format_results(results[:20]))
Pattern 5: Recent Papers
User asks: "What did I recently add?"
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, format_results
library = ZoteroLibrary()
results = library.get_recent(limit=20)
print(format_results(results))
Pattern 6: Multi-Term OR Search (Multiple Spellings/Languages)
User asks: "Find papers about Atyal or 泰雅族" (alternate spellings/languages)
IMPORTANT: Zotero treats multi-word queries as AND conditions. For OR searches, search each term separately and merge.
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results
orchestrator = SearchOrchestrator()
# Search each term separately
all_results = {}
for term in ['Atayal', '泰雅族']:
results = orchestrator.comprehensive_search(term, max_results=50, use_semantic=True)
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')
# Optional: Filter for specific content
filtered = [item for item in ranked
if any(term in (item.title + item.abstract).lower()
for term in ['atyal', 'atayal', '泰雅'])]
print(format_results(filtered[:25], include_abstracts=True))
Pattern 7: Multi-Topic AND Search
User asks: "Find papers about both cognition and learning"
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results
orchestrator = SearchOrchestrator()
# Search both topics
results1 = orchestrator.comprehensive_search("cognition", max_results=30)
results2 = orchestrator.comprehensive_search("learning", max_results=30)
# Find intersection
keys1 = {item.key for item in results1}
keys2 = {item.key for item in results2}
common_keys = keys1 & keys2
if common_keys:
common_items = [item for item in results1 if item.key in common_keys]
print("Papers about both topics:")
print(format_results(common_items))
else:
print("No papers found on both topics.")
print("\nCognition results:")
print(format_results(results1[:10]))
print("\nLearning results:")
print(format_results(results2[:10]))
🔧 Advanced Usage
Custom Filtering Logic
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, SearchOrchestrator, format_results
library = ZoteroLibrary()
orchestrator = SearchOrchestrator(library)
# Fetch large dataset
items = library.search_items("neural networks", limit=100)
# Custom filtering
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[:15]))
Multi-Angle Custom Search
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, SearchOrchestrator, format_results
library = ZoteroLibrary()
orchestrator = SearchOrchestrator(library)
all_results = set()
# Multiple search angles
queries = [
"skill transfer",
"transfer of learning",
"generalization of skills"
]
for query in queries:
results = library.search_items(query, limit=30)
all_results.update(results)
# Rank combined results
ranked = orchestrator._rank_items(list(all_results), "skill transfer")
print(format_results(ranked[:20]))
Iterative Refinement
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import ZoteroLibrary, SearchOrchestrator, format_results
library = ZoteroLibrary()
orchestrator = SearchOrchestrator(library)
# Initial search
initial = library.search_items("memory", limit=50)
# Analyze tags
tag_freq = {}
for item in initial:
for tag in item.tags:
tag_freq[tag] = tag_freq.get(tag, 0) + 1
# Find most common tag
if tag_freq:
most_common_tag = max(tag_freq, key=tag_freq.get)
# Refine search
refined = orchestrator.filter_by_criteria(
initial,
required_tags=[most_common_tag]
)
print(f"Papers with most common tag '{most_common_tag}':")
print(format_results(refined))
📚 API Reference
SearchOrchestrator
Main class for automated searching.
comprehensive_search(query, max_results=20, use_semantic=True, use_keyword=True, use_tags=True, search_limit_per_strategy=50)
Performs multi-strategy search with automatic deduplication and ranking.
Parameters:
- •
query(str): Search query - •
max_results(int): Maximum results to return (default: 20) - •
use_semantic(bool): Use semantic search (default: True) - •
use_keyword(bool): Use keyword search (default: True) - •
use_tags(bool): Use tag search (default: True) - •
search_limit_per_strategy(int): Items to fetch per strategy (default: 50)
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.
Parameters:
- •
items(list): Items to filter - •
item_types(list): Allowed item types (e.g., ["journalArticle"]) - •
date_range(tuple): (min_year, max_year) - •
required_tags(list): Tags that must be present - •
excluded_tags(list): Tags that must not be present
Returns: Filtered list of ZoteroItem objects
ZoteroLibrary
Low-level interface to Zotero.
search_items(query, qmode="titleCreatorYear", item_type="-attachment", limit=100, tag=None)
Basic keyword search.
semantic_search(query, limit=100, search_type="hybrid")
Semantic/vector search.
search_by_tag(tags, item_type="-attachment", limit=100)
Search by tags.
get_recent(limit=50)
Get recently added items.
get_tags()
Get all tags in library.
format_results(items, include_abstracts=True, max_abstract_length=300)
Format items as markdown.
⚙️ Configuration
Default Parameters
Good defaults for most searches:
orchestrator.comprehensive_search(
query,
max_results=20, # Top 20 results
search_limit_per_strategy=50 # Fetch 50 per strategy
)
Adjusting Search Depth
For quick searches (fewer results, faster):
results = orchestrator.comprehensive_search(
query,
max_results=10,
search_limit_per_strategy=20
)
For thorough searches (more comprehensive):
results = orchestrator.comprehensive_search(
query,
max_results=30,
search_limit_per_strategy=100
)
🔍 How It Works
Behind the Scenes
When you call comprehensive_search("embodied cognition", max_results=20):
- •
Semantic Search (if enabled):
- •Searches "embodied cognition" (hybrid mode) → 50 items
- •Searches "embodied cognition" (vector mode) → 50 items
- •
Keyword Search (if enabled):
- •Searches with qmode="everything" → 50 items
- •Searches with qmode="titleCreatorYear" → 50 items
- •
Tag Search (if enabled):
- •Extracts words from query
- •Finds matching tags in library
- •Searches by matching tags → 50 items
- •
Processing:
- •Combines all results (~250 items)
- •Deduplicates using item keys (~120 unique)
- •Ranks by relevance score
- •Returns top 20
- •
Context:
- •Only the final 20 items go to LLM context
- •All processing happens in code execution environment
Why This Is Better
Old Approach (Direct MCP):
# 5+ function calls, all results to context
results1 = zotero_semantic_search("query", limit=10) # Crash risk if > 15
results2 = zotero_search_items("query", limit=10)
# ... manual deduplication, no ranking
# All items (50+) load into context
New Approach (Code Execution):
# 1 function call, only top results to context
results = orchestrator.comprehensive_search("query", max_results=20)
# Fetches 250+ items, processes in code, returns top 20
🛠️ Error Handling
Always handle potential errors:
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results
orchestrator = SearchOrchestrator()
try:
results = orchestrator.comprehensive_search("query", max_results=20)
if results:
print(format_results(results))
else:
print("No results found. Try a broader search term.")
except Exception as e:
print(f"Search failed: {e}")
print("Please check your Zotero MCP configuration.")
📖 Examples
See /Users/niyaro/Documents/Code/zotero-code-execution/examples.py for 8 complete working examples.
🎓 Quick Reference
| Task | Code |
|---|---|
| Basic search | orchestrator.comprehensive_search(query, max_results=20) |
| Filter by type | orchestrator.filter_by_criteria(items, item_types=["journalArticle"]) |
| Filter by date | orchestrator.filter_by_criteria(items, date_range=(2020, 2025)) |
| Search author | library.search_items(author, qmode="titleCreatorYear", limit=50) |
| Search by tag | library.search_by_tag([tags], limit=50) |
| Recent items | library.get_recent(limit=20) |
| Format output | format_results(items, include_abstracts=True) |
💡 Tips
- •Start simple: Use
comprehensive_search()for most queries - •Adjust depth: Use
search_limit_per_strategyto control thoroughness - •Filter after: Fetch broadly, filter in code
- •Custom logic: Use Python for complex filtering
- •Check errors: Always wrap in try/except
📁 Documentation
- •Quick Start:
/Users/niyaro/Documents/Code/zotero-code-execution/QUICK_START.md - •Full Docs:
/Users/niyaro/Documents/Code/zotero-code-execution/README.md - •Examples:
/Users/niyaro/Documents/Code/zotero-code-execution/examples.py - •Status:
/Users/niyaro/Documents/Code/zotero-code-execution/HONEST_STATUS.md
⚠️ Important Notes
- •This uses code execution, not direct MCP calls
- •Large datasets are processed in code, keeping context small
- •Semantic search may not be available (falls back to keyword)
- •Results are automatically deduplicated and ranked
- •Safe to use large limits (100+) because filtering happens in code
🔄 Migration from zotero-mcp
Old pattern:
# Multiple manual MCP calls
results1 = zotero_semantic_search("query", limit=10)
results2 = zotero_search_items("query", limit=10)
# Manual deduplication...
New pattern:
# One function call with code execution
import sys
sys.path.append('/Users/niyaro/Documents/Code/zotero-code-execution')
import setup_paths
from zotero_lib import SearchOrchestrator, format_results
orchestrator = SearchOrchestrator()
results = orchestrator.comprehensive_search("query", max_results=20)
print(format_results(results))
Remember: This skill uses code execution to safely handle large searches. The implementation is in /Users/niyaro/Documents/Code/zotero-code-execution/.