"Qdrant MCP Server" matching MCP servers:

Enables semantic and hybrid search over documents and codebases backed by a Qdrant vector database, with collection management, document CRUD, metadata filtering, and AST-aware code indexing using local or cloud embedding providers. Supports natural language queries, incremental re-indexing of changed files, and custom configurable prompts for guided workflows.

Provides semantic search capabilities using Qdrant vector database with multiple embedding providers, including hybrid search, code indexing, and git history search. Adds optional time-based recency scoring to search results.

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.

A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.

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

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

Indexes codebases into Qdrant for semantic search, enabling AI assistants to find relevant code by meaning without re-exploring the repo.

Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.

A Model Context Protocol server that provides semantic understanding of codebases using Qdrant vector database, enabling AI assistants to search files by purpose, discover relationships between files, analyze architecture, and identify refactoring opportunities.

Self-hosted semantic code search for AI coding agents. Indexes many repositories at once with tree-sitter AST chunking, local llama.cpp embeddings and Qdrant, so Claude Code, Cursor, Codex and any MCP client can find code by meaning, trace usages and callers, and read the git history behind each chunk. No API keys, and your code never leaves your machine.

A composable semantic memory layer that provides cross-project recall and session context using Qdrant and OpenAI embeddings. It enables users to securely store, search, and manage persistent memories with built-in secret scrubbing for privacy.

A high-performance MCP server for semantic search and codebase indexing using the Qdrant vector database. It features optimized embedding pipelines, AST-aware chunking, and git metadata enrichment for fast, privacy-focused local or remote search.

Helps developers tackle legacy codebases by giving AI coding assistants context through Qdrant vector store and Agentic RAG with Ollama or DeepSeek.

Enables agents to store text and metadata and later retrieve it by plain-language similarity search via Qdrant, exposing store and find tools. Embeddings are generated locally, so no external embedding service is required.

MCP server for persistent memory using Qdrant vector store. Stores text memories with LLM-generated embeddings and retrieves them via semantic search.

A local RAG-powered documentation search system that uses vector embeddings and Qdrant to enable semantic search across markdown, HTML, and other file formats. It provides an MCP interface for AI tools like Cursor to intelligently query and retrieve information from local knowledge bases.

Enables centralized access to Firecrawl, Exa, Qdrant, embeddings, Playwright browser automation, and MCP clients through a single token-protected HTTP and MCP interface.

A Model Context Protocol server that provides RAG capabilities for markdown documents using Qdrant for vector storage and Ollama for embeddings, enabling semantic search and document ingestion directly from Cursor IDE.

Enables MCP clients to index code repositories and run AST-aware semantic and hybrid keyword search across files, commits and branches, returning relevant code snippets with expanded file context. It handles incremental re-indexing with local embeddings and Qdrant storage, and can serve either over stdio or as a bearer-token-protected HTTP endpoint for remote or containerized use.

Universal MCP server for readonly-first access to Oracle, SQL Server, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, and Qdrant vector search.