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Sync WeRead data into DuckDB for SQL analysis and MCP-based joins.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "weread-shelf" yet — see the docs or source repo.
Using my WeRead data already synced into DuckDB, analyze my reading volume over the past year, author distribution, and category preferences, and show the SQL approach.
A summary of the statistics, plus example SQL queries used to analyze the WeRead data.
Query my WeRead library for the authors with the highest reading count or saved books, sorted by count.
A ranked list of authors with an explanation of the SQL query results.
Join my WeRead data with another table already in DuckDB and find books that appear in both sources.
The joined results, along with an example SQL query for the cross-source join.
Data analysts, researchers, or heavy readers can sync WeRead data into DuckDB and use SQL to measure reading volume, book distribution, and preferences. It is useful for personal reading reviews and long-term tracking.
Developers can expose WeRead data to agents through an MCP server and drive analysis with natural language. This is useful for building Q&A or analytical assistants around personal bookshelf data.
If you already maintain other tables in DuckDB, you can join them with WeRead data to find overlaps or relationships. This is useful for cross-source catalog comparison and data consolidation.
It syncs WeRead data into DuckDB so you can query it with SQL. It also provides an MCP server for agent-based analysis and cross-source joins.
From the description, you at least need a DuckDB environment and an MCP-compatible setup to use the server capability. For exact installation steps, runtime requirements, or authentication details, see the source repository.
It not only supports SQL on WeRead data, but specifically syncs that data into DuckDB and exposes it through MCP for agents. Another distinguishing feature is support for cross-source joins.
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