"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.

Provides hybrid search (BM25 + vector) and staleness reporting for local markdown notes, exposing search and indexing tools to AI assistants via MCP.

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

Enables fast local hybrid search over a folder of markdown files via MCP, combining BM25 and vector similarity with explicit relevance verdicts and no LLM or network calls at query time.

Enables natural-language semantic search over your own local files through an MCP server, fully offline without API keys or a server daemon, with optional LLM-grounded answers and exact file:line sources.

Standalone MCP server for Obsidian vaults - hybrid search (FTS5 + vector + cross-encoder reranking), images and PDFs in agent-readable form, Kanban-aware tasks (Tasks-plugin + Dataview formats), structured memory with topic recall, fine-grained read/write tools for optimal token efficiency, and link graph support. Run locally, self-host, or one-click deploy for remote access. 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.

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

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.

Enables Claude, Cursor, Windsurf, and autonomous agents to save web pages and notes as persistent memories, then search them using full-text keyword matching and semantic vector recall.

Enables MCP clients to search a folder of markdown notes and read individual notes through two read-only tools, using hybrid dense-vector and BM25 retrieval.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.

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.

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

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.

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

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.

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.

Enables AI coding agents to save and recall atomic markdown knowledge cards connected by bidirectional links, giving them persistent memory that carries insights across sessions instead of starting from scratch. Works with Claude Code, Cursor, VS Code Copilot, Codex, Windsurf, and any MCP client using plain markdown and git sync rather than a vector database.

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 AI coding agents to persist and recall atomic markdown knowledge cards across sessions via MCP tools, with bidirectional links and git sync, without a vector database.

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.

Enables local semantic and full-text search over Markdown notes through MCP, returning relevant passages ranked by meaning and exact terms without requiring a GPU or hosted APIs.

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.

Gives AI coding agents persistent memory across sessions by writing each task's insights as atomic markdown knowledge cards with bidirectional links and recalling relevant ones before new work begins. Cards are shared across Claude Code, Cursor, Copilot, Codex, Windsurf and other MCP clients, and can be synced across devices via git — no vector database or embeddings required.

Enables AI clients to read, write, search, and manage files in an Obsidian vault via MCP over SSE, running standalone without the Obsidian desktop app.