"Qdrant MCP Server" matching MCP servers:

Enables AI assistants to interact with a Qdrant vector database, including embedding generation and vector search operations.

Enables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.

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

MCP server for document ingestion and semantic search on Qdrant. Enables ingesting local documents, generating embeddings with OpenAI, and performing vector search with metadata filters.

3D similarity dashboard for Qdrant + read-only MCP server (9 tools). Python stdlib only.

Enables searching enterprise wiki content via vector embeddings, using Qdrant for storage and FastMCP for SSE-based tool exposure.

Provides RAG search and ask capabilities over markdown and text files using Qdrant vector database, with tools for ingestion, retrieval, and source management.

Exposes hybrid retrieval (dense embeddings + BM25 + RRF) and document operations (search, fetch, rerank) as MCP tools, using Qdrant and OpenAI embeddings for local or server mode.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

A local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

Enables reading news numerologically by extracting numbers, computing gematria, finding resonance patterns via hybrid search in Qdrant, and generating daily numerology forecasts.

Enables an AI assistant to search scientific literature across OpenAlex and Semantic Scholar, download supported Elsevier and Springer Nature full texts, and build a local searchable index so questions can be answered with citations to original paper passages. It runs locally over stdio with CPU-based embeddings and needs no Qdrant account, Docker, GPU, or LLM API key of its own.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Enables Claude Desktop and compatible agents to search, read, add, update, and delete a locally stored, user-curated memory using hybrid retrieval with reranking and Qdrant. All processing runs locally without cloud services or API keys.

Q-learning memory for Claude Code. Persistent memory that learns which context helps you get work done. Memories that lead to productive sessions (commits, PRs, tests) earn higher retrieval rank automatically. 16 MCP tools, hybrid BM25 + vector + Q-value scoring, local-first with Qdrant + FastEmbed.

Turns a plain-Markdown Obsidian vault into a second brain that any MCP-speaking coding agent can search, read, extend, and maintain, using swappable SQLite, Qdrant, or markdown memory backends with automatic re-indexing and wiki-link graph navigation. Exposes read, write, and index tools plus an operator CLI for health, linting, and graph export.

Enables indexing and semantic search of codebases and documents via MCP, using Ollama embeddings and Qdrant vector store.

Local long-term memory for AI assistants, written in Rust: hybrid search with reranking over a local Qdrant, a fact store that resolves contradictions, and a hash-chained audit log. Runs fully offline, no API keys.

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.