"Understanding Vector Search" matching MCP servers:

Enables local hybrid search over Obsidian and Markdown vaults via MCP, combining vector retrieval, full-text search, reranking, graph navigation, and safe CRUD while keeping data local.

Enables AI assistants to securely search and retrieve information from a user's encrypted Joplin notebook via vector memory and MCP.

MCP server for Obsidian vaults — search, memory, link graph, 23 tools, OAuth-protected. Runs locally via Docker or remotely with Obsidian Sync + OAuth 2.1.

Enables deterministic, token-minimal multi-hop retrieval from a vault of [[wikilink]] notes via the Model Context Protocol, allowing LLMs to answer questions by following authored links without vector databases or LLM calls during retrieval.

A generic Markdown vault MCP server with FTS5 full-text search, semantic vector search, frontmatter-aware indexing, incremental reindexing, and non-markdown attachment support that exposes search, read, write, and edit tools.

Enables LLMs to search and retrieve personal journal entries using RAG, and add new entries with date filtering and multiple vector stores.

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

Provides a lightweight personal knowledge base MCP server that compiles raw materials into interconnected Wiki pages, with hybrid search (BM25, optional vector, link expansion) and tools for querying, reading, writing, and ingesting content.

Read-only semantic retrieval for agents that need to find the right Obsidian note without write access.

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Enables semantic search over local markdown note collections using vector embeddings, with real-time file watching and zero-config setup.

A Model Context Protocol server that enables semantic vector search over Obsidian notes, allowing AI assistants to intelligently search and retrieve relevant note content.

An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.

Provides a hybrid search engine for Obsidian vaults, enabling LLM agents to query notes with BM25 keyword and vector semantic search, metadata filtering, and sibling-document retrieval.

MCP server that provides secure access to a serverless Markdown wiki on AWS, enabling AI assistants to read, search, and edit pages with space-level permission checks and Bedrock-powered semantic search.

Enables MCP clients to search, read, and list notes and tags in a Markdown vault using read-only tools.

Enables Claude to search and analyze Obsidian vault content using ripgrep with support for wiki links, backlinks, frontmatter properties, orphaned notes, and intelligent context detection showing which heading or property contains each match.

A MCP server for managing sticky notes with CRUD operations, search, tags, conversations, and a React UI.

An Obsidian plugin that runs an MCP server, enabling external LLM tools to read, search, create, and modify notes in your vault via HTTP or stdio transport.

Read-only MCP server that lets AI agents query SageRead data: books, reading progress, stats, notes, AI chats, papers, and semantic vector search, with API keys staying inside the app. Supports both Better SageRead and upstream SageRead data directories.