"Qdrant MCP Server" matching MCP servers:

Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.

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.

Enables AI assistants to perform semantic search and retrieval across codebases stored in Qdrant, using Ollama models for GPU-accelerated embeddings.

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

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.

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.

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.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

Semantic search server for code and documentation using Qdrant vector database. Supports multi-language indexing, live updates, and natural language queries.

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.

Enables semantic search across your codebase using Google's Gemini embeddings and Qdrant Cloud vector storage. Supports 15+ programming languages with smart code chunking and real-time file change monitoring.

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

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.

Allows agents to lookup code snippets in your project. Uses a remote MCP server with a collection of code snippets, with support for automatic project language detection.