"Exploring RAG through defined workspaces" matching MCP servers:

Enables AI editors to search past Zenn articles and book chapters, get article contents, list topics, receive related-link suggestions, and sync the vector index directly through MCP tools.

Enables local retrieval-augmented generation document search for Cline by providing semantic search over indexed files, listing indexed sources, and reporting index status through MCP tools.

Enables AI agents to connect local knowledge bases, preview and index selected directories, search for traceable snippets with source metadata, and read indexed document versions through MCP tools.

Provides read-only retrieval of STM32 datasheet and reference manual content with exact citations, page numbers, register details, and source hashes so Claude can answer questions or generate code from verified evidence.

Enables question answering over technical documents (Markdown, TXT, PDF) using hybrid retrieval (vector + lexical) with cited sources and audit logging, fully locally with optional cloud fallback.

Provides read-only RAG retrieval, document access, source auditing, status, and health checks for CUMCM optimization and decision modeling contexts.

Retrieval-Augmented Generation system serving LangGraph documentation through the Model Context Protocol, enabling semantic search and context-aware responses.

Enables coding agents to retrieve relevant sections from a repository's declared documentation scope using hybrid vector and full-text search, with incremental indexing.

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.

A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.

Enables cited, revision-aware retrieval over real IETF RFC chunks with zero LLM in the answer path, including graph-based supersession resolution and relationship queries via MCP tools.

MCP server for RAG-based search over 1C Enterprise configuration documentation, enabling natural language queries to find objects like справочники, документы, and отчеты.

Enables MCP clients to semantically search a local folder of markdown documents via a single search_notes tool, with embeddings generated locally by Ollama and support for excluding superseded or archived material.

Enables Claude Code (or any MCP client) to run hybrid vector and keyword search over a locally indexed Confluence Data Center / Server instance, retrieve full page text, check index status, and trigger background refreshes.

MCP server exposing a RAG knowledge base as read-only tools (search_knowledge_base, ask_knowledge_base, kb_stats, kb_diagnostic) for AI clients like Claude Desktop and Cursor, enabling token-efficient document retrieval and Q&A.

Provides read-only MCP tools for hybrid semantic and keyword search over locally indexed PDF documentation, with citations and context retrieval for LLM agents.

An MCP server that enables users to query Kedro framework documentation using retrieval-augmented generation. It builds a local knowledge base from documentation files to help users navigate and apply Kedro's data science pipeline framework.

Standalone MCP server exposing agentic-RAG tools (hybrid search, article retrieval, category listing) over a pgvector-backed knowledge base with bearer-token auth, designed for deployment on Render.

Enables users to ask natural-language questions over FAQ markdown files and receive grounded, source-cited answers through the ask_faq tool over stdio.