Qdrant Vector Database
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.
What is Qdrant Vector Database?
Qdrant Vector Database is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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-pow...
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.
This server falls under the Databases category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Enables semantic search and document management using a loca
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx qdrant-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Qdrant Vector Database
The Qdrant MCP Server connects AI assistants to a local Qdrant vector database, enabling semantic and hybrid search over documents and codebases using embedding providers like OpenAI, Cohere, Voyage, or a local Ollama model. It exposes tools for collection management, document ingestion, AST-aware code vectorization across 35+ programming languages, and Git history indexing. Developers use it to give their AI assistant a persistent, searchable memory of their codebase and project documents without relying on external cloud search services.
Prerequisites
- Node.js 18+ (Node 24.x requires CXXFLAGS='-std=c++20' during install)
- Docker or Podman to run the Qdrant vector database container
- An embedding provider: local Ollama with nomic-embed-text pulled, or an API key for OpenAI, Cohere, or Voyage
- Git (optional, for Git history indexing features)
- An MCP-compatible client such as Claude Desktop or the Claude CLI
Clone and install dependencies
Clone the repository and install Node.js dependencies. On Node 24.x you must set the C++ standard flag before installing.
git clone https://github.com/mhalder/qdrant-mcp-server.git
cd qdrant-mcp-server
npm install
# Node 24.x only:
# CXXFLAGS='-std=c++20' npm installStart the Qdrant vector database
Use the included Docker Compose file to launch a local Qdrant instance. If using Ollama for embeddings, also pull the embedding model.
docker compose up -d
# For Ollama embeddings:
docker exec ollama ollama pull nomic-embed-textBuild the server
Compile the TypeScript source into the build directory before running.
npm run buildConfigure environment variables
Set the embedding provider and any required API keys. QDRANT_URL defaults to http://localhost:6333. EMBEDDING_PROVIDER can be ollama, openai, cohere, or voyage.
export EMBEDDING_PROVIDER=openai
export OPENAI_API_KEY=sk-...
export QDRANT_URL=http://localhost:6333
# Optional:
export LOG_LEVEL=infoRegister the server with your MCP client
Add the server to your Claude Desktop config file or use the Claude CLI to register it over stdio.
claude mcp add --transport stdio qdrant -- node /path/to/qdrant-mcp-server/build/index.jsIndex your codebase
Once connected, ask your AI assistant to index a project directory. The server will chunk and embed your code using AST-aware splitting.
Qdrant Vector Database Examples
Client configuration
Claude Desktop config using the stdio transport with OpenAI embeddings.
{
"mcpServers": {
"qdrant": {
"command": "node",
"args": ["/path/to/qdrant-mcp-server/build/index.js"],
"env": {
"EMBEDDING_PROVIDER": "openai",
"OPENAI_API_KEY": "sk-...",
"QDRANT_URL": "http://localhost:6333"
}
}
}
}Prompts to try
Example prompts once the server is connected and a collection or codebase has been indexed.
- "Index the codebase at /home/user/myproject and create a collection called myproject"
- "Search the myproject collection for code related to authentication middleware"
- "Find all usages of the UserService class across the indexed codebase"
- "Search the Git history for commits that modified the payment module"
- "List all available collections in my Qdrant instance"Troubleshooting Qdrant Vector Database
npm install fails on Node 24.x with a C++ compilation error
Set the C++ standard flag before installing: CXXFLAGS='-std=c++20' npm install
Embedding calls fail with an authentication error
Ensure the correct API key environment variable is set for your chosen provider: OPENAI_API_KEY, COHERE_API_KEY, or VOYAGE_API_KEY. Confirm EMBEDDING_PROVIDER matches the key you provided.
Qdrant connection refused at http://localhost:6333
Run 'docker compose up -d' from the repository root to start the Qdrant container, then verify it is healthy with 'docker ps'.
Frequently Asked Questions about Qdrant Vector Database
What is Qdrant Vector Database?
Qdrant Vector Database is a Model Context Protocol (MCP) server that 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. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Qdrant Vector Database?
Follow the installation instructions on the Qdrant Vector Database GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Qdrant Vector Database?
Qdrant Vector Database works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Qdrant Vector Database free to use?
Yes, Qdrant Vector Database is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
Qdrant Vector Database Alternatives — Similar Databases Servers
Looking for alternatives to Qdrant Vector Database? Here are other popular databases servers you can use with Claude, Cursor, and VS Code.
Excelize
★ 20.6kGo language library for reading and writing Microsoft Excel™ (XLAM / XLSM / XLSX / XLTM / XLTX) spreadsheets
MCP Toolbox for Databases
★ 15.3kOpen source MCP server specializing in easy, fast, and secure tools for Databases.
DBHub
★ 2.8kA universal database gateway MCP server that enables AI assistants to connect to and query multiple databases (PostgreSQL, MySQL, MariaDB, SQL Server, SQLite) with support for schema exploration, SQL execution, and secure connections via SSH tunnels.
Tabularis
★ 2.1kA lightweight, cross-platform database client for developers. Supports MySQL, PostgreSQL and SQLite. Hackable with plugins. Built for speed, security, and aesthetics.
Postgres AI Guide
★ 1.7kMCP server and Claude plugin for Postgres skills and documentation. Helps AI coding tools generate better PostgreSQL code.
Anyquery
★ 1.7k🏎️ 🏠 ☁️ - Query more than 40 apps with one binary using SQL. It can also connect to your PostgreSQL, MySQL, or SQLite compatible database. Local-first and private by design.
Browse More Databases MCP Servers
Explore all databases servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.
Set Up Qdrant Vector Database in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
Ready to use Qdrant Vector Database?
Browse our complete directory of 33,000+ MCP servers, read setup guides for your editor, and start building with the Model Context Protocol.