"Qdrant MCP Server" matching MCP servers:

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

An MCP server for storing and retrieving memories using Qdrant vector search, acting as a semantic memory layer.

An MCP server that provides long-term memory and semantic search using Qdrant and OpenAI embeddings, with tools for storing, searching, and managing knowledge.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

MCP server that gives AI coding agents persistent, semantic memory via Qdrant vector search, enabling workspace-aware codebase, documentation, and decision search.

Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Enables persistent memory storage and retrieval for AI conversations using Mem0, with semantic search capabilities backed by local Postgres and Qdrant vector database.

A local MCP server that exposes Mem0 as persistent memory tools to any MCP client, storing data locally with no cloud API or API key required.

Project-scoped semantic workspace memory for AI coding assistants. Watches your project files and auto-indexes code and docs into Qdrant using tree-sitter semantic chunking and hybrid search (dense + sparse + RRF)

Generic MCP server exposing Nexusm core capabilities (memory, conversation, knowledge, feedback, context) to MCP clients via stdio and Streamable HTTP transports.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

A forkable RAG project with an MCP server for VS Code/Copilot, enabling indexing and querying of knowledge from URLs, PDFs, or TeX files.

MCP server for flomo that enables AI tools to quickly add notes to your flomo account via the API.

An MCP server implementation that enables interaction with the Letta API for managing agents, memory blocks, and tools in the Letta system.

TypeScript MCP server for Obsidian with core vault operations, graph analytics, and semantic search.

An MCP server enabling LLMs to access WeChat Reading bookshelf, notes, highlights, and reviews via tools like get_bookshelf, search_books, and get_book_notes_and_highlights.

MCP server for managing personal items with expiration dates, enabling AI agents to add, query, and scan barcode items via API Key authentication.

A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling.