Provides read-only MCP tools for searching and asking over private documents via a local RAG service (reed), returning ranked passages with citations while keeping data on the machine.
Provides tools for ingesting documents into a local vector database and retrieving relevant information via semantic search, enabling retrieval-augmented generation for MCP clients.
Provides read-only, citation-backed semantic search and retrieval-augmented generation over enterprise documents via standardized MCP tools, with local embeddings for privacy.
Provides 11 MCP tools for deterministic, local semantic search over your documents, including indexing, retrieval, exact-match facets, temporal truth, semantic diff, and agent-first JSON output. Enables LLMs and agents to search, retrieve, and analyze documents without cloud dependencies or per-query costs.
Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.