zotero-code-execution

Zotero Code Execution is a Python library that implements Anthropic's code execution pattern for comprehensive Zotero searches without crashes or token bloat. It performs semantic, keyword, and tag-based searches in a single orchestrated call, automatically deduplicates results, and ranks them by relevance. The library processes 100+ items in the execution environment while returning only the top results to your context, solving the crash risk and manual orchestration problems of direct MCP tool calls. Researchers, operations teams, and knowledge workers benefit from efficient bibliographic discovery integrated with Python workflows and Claude Code automation.

[{"step":"Set up Zotero MCP and Python environment.","action":"Install the Zotero MCP server (https://github.com/modelcontextprotocol/servers/tree/main/src/zotero) and ensure Python 3.10+ is installed. Use `pip install pyzotero pandas` to install required libraries.","tip":"Verify MCP server connectivity by running `mcp-zotero --list-collections` in your terminal."},{"step":"Define your research topic and strategies.","action":"Replace [RESEARCH_TOPIC] with your focus area (e.g., 'climate change mitigation technologies'). For [STRATEGY_1], [STRATEGY_2], and [STRATEGY_3], use combinations of keyword searches, citation chaining, semantic tagging, or author-based queries.","tip":"For citation chaining, start with a highly cited paper in your field and use its forward/backward citations as a strategy."},{"step":"Execute the code and generate output.","action":"Run the Python script (see example below) to execute the search, extract metadata, and generate the CSV. Adjust [OUTPUT_FILENAME] to your preferred name.","tip":"Use `pandas` to pre-filter results by publication year or relevance score before final output."},{"step":"Review and refine results.","action":"Open the generated CSV file and manually review items with missing semantic tags. Use Zotero's tagging interface to add missing tags for better organization.","tip":"Leverage Zotero's 'Related Items' feature to identify additional papers for inclusion."},{"step":"Integrate with further analysis.","action":"Import the CSV into a data analysis tool (e.g., Jupyter Notebook, Tableau) to visualize citation networks, co-authorship patterns, or keyword trends.","tip":"Use `networkx` in Python to generate a citation graph for deeper insights."}]