Enables semantic search across personal PDF paper collections with page-level citations, allowing users to query their library from any MCP-capable client.
A local academic research assistant that indexes PDFs into a searchable vector library and exposes MCP tools for semantic search, claim extraction, contradiction detection, and multi-step research synthesis.
Enables users to build and query a private knowledge base by uploading documents, which are embedded and stored locally, then accessible via MCP for semantic search and retrieval.
Transforms PDF collections into a searchable knowledge base using TF-IDF indexing and proximity matching. It enables users to search documents, retrieve specific page content, and manage document libraries through natural language via MCP clients.