Enables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.
Provides RAG search and ask capabilities over markdown and text files using Qdrant vector database, with tools for ingestion, retrieval, and source management.
Enables RAG-based querying of local stock company data using a local LLM and vector database, providing tools to ask questions, search raw chunks, and list documents.
Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.
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.