"Slack Bot with RAG Document Retrieval and Automated Response System" matching MCP servers:

Enables semantic search and reading of Obsidian Markdown notes through read-only MCP tools, allowing Claude to retrieve relevant passages from a local vault.

A no-server MCP toolkit that organizes project wikis from Feishu documents, messages, and more, enabling AI clients to answer questions with cited evidence to prevent hallucinations.

A local AI document assistant MCP server that enables listing, reading, and editing documents via tools, resources, and prompts, allowing LLMs to manage document workflows through natural language.

Enables natural-language semantic search over a fully local knowledge base, keeping embeddings, storage, and retrieval on the machine.

Provides semantic search over Obsidian markdown notes using BAAI/bge-m3 embeddings and ChromaDB, accessible via the Model Context Protocol.

MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.

Enables semantic and keyword search over Obsidian-style Markdown knowledge bases by registering any folder as a vault, with support for incremental indexing, raw source reads, and cache-backed embeddings over MCP stdio.

Enables automatic collection and AI analysis of Slack messages to create organized Notion pages, supporting custom analysis directions like meeting minutes, issue extraction, and topic classification.

MCP server for hybrid retrieval over markdown vaults, combining sqlite-vec embeddings and FTS5 keywords with reciprocal rank fusion, plus optional cross-encoder reranking.

Enables notes management, weather and news search, document ingestion and RAG-based semantic search using GroundX, with OpenAI GPT integration for summarization and completions.

Enables semantic search over an Obsidian vault using natural language, retrieving relevant notes and extracted conclusions.

Answers questions about a folder of markdown notes using hybrid retrieval that fuses FTS5 keyword search and vector search, then reranks results with a cross-encoder.

Provides local hybrid search over an Obsidian vault via the Model Context Protocol. It enables Claude Code to retrieve relevant notes with citations, using on-device embeddings and BM25 rank fusion.

Enables semantic search and content retrieval from local Obsidian vaults through the Model Context Protocol. It allows AI assistants to query notes by meaning, filter by tags, and access full note content for enhanced knowledge integration.

Enables semantic search and retrieval over an Obsidian vault using local or API-based embeddings, allowing AI assistants to find notes by meaning, get related content, and pull context during conversations.

Enables natural-language and keyword search across Obsidian notes, returning reranked passages with provenance, and supports reading notes and incremental indexing.

Enables users to connect their own account to AI clients to list notebooks and sources, fetch complete indexed source text and summaries, and ask grounded questions that return citation-to-source mappings, without creating, renaming, uploading, sharing, or deleting content.

A Python MCP server for note management with RAG capabilities, enabling adding, searching, asking questions, summarizing, and deleting notes, using PostgreSQL and ChromaDB with Google Gemini for embeddings and generation.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

Provides tools for indexing documents, creating notes, searching content, and extracting metadata to enable document processing and knowledge management.