Claude Qdrant
Local-first TypeScript MCP server for Qdrant with client isolation, LM Studio integration, and scalable document workflows.
What is Claude Qdrant?
Claude Qdrant is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to local-first typescript mcp server for qdrant with client isolation, lm studio integration, and scalable document workflows.
Local-first TypeScript MCP server for Qdrant with client isolation, LM Studio integration, and scalable document workflows.
This server falls under the Databases category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Local-first TypeScript MCP server for Qdrant with client iso
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx claude-qdrantConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Claude Qdrant
Claude Qdrant MCP is a local-first TypeScript MCP server that bridges Claude and other AI clients with a Qdrant vector database, enabling semantic search and RAG (retrieval-augmented generation) workflows entirely on your own hardware. It integrates with LM Studio to generate embeddings using models like BGE-M3 and supports per-client collection isolation, making it ideal for managing separate knowledge bases for different projects or customers. Developers use it to build private, scalable document retrieval systems without sending data to third-party embedding APIs.
Prerequisites
- Node.js 18+ installed
- Qdrant server running locally (Docker recommended) or a Qdrant Cloud instance
- LM Studio installed and running with a BGE-M3 embedding model and an LLM such as Qwen3-8B loaded
- npm available in your PATH
- An MCP-compatible client such as Claude Desktop
Install the claude-qdrant-mcp package
Install the package globally so the `qdrant-setup` wizard and MCP server command are available.
npm install -g claude-qdrant-mcpCreate a project directory and run setup
Create a dedicated directory for your RAG project and run the interactive setup wizard which generates a .env file with all required configuration.
mkdir my-rag-project && cd my-rag-project
qdrant-setupConfigure the .env file
Edit the generated .env file with your Qdrant URL, LM Studio endpoints, embedding model, and client collection names.
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your-api-key-if-using-cloud
LM_STUDIO_URL=http://127.0.0.1:1235
EMBEDDING_MODEL=text-embedding-finetuned-bge-m3
EMBEDDING_DIM=1024
LLM_MODEL=qwen/qwen3-8b
CLIENT_COLLECTIONS=client_a,client_b,personal,work
CONCURRENCY=5
BATCH_SIZE=10
CHUNK_SIZE=500
CHUNK_OVERLAP=10Seed documents into a collection
Use the seed command to ingest documents from a local directory into a named client collection. The server chunks, embeds, and stores them in Qdrant automatically.
npm run seed -- --client work --filesdir /path/to/your/documents
# Overwrite existing collection:
npm run seed -- --client personal --filesdir /docs --overwriteTest the connection
Verify Qdrant and LM Studio are reachable and collections are populated before connecting your MCP client.
npm run test-connectionAdd to your MCP client configuration
Register the MCP server in Claude Desktop so it launches when you start a session.
Claude Qdrant Examples
Client configuration
Claude Desktop configuration for the claude-qdrant MCP server pointing at your project directory.
{
"mcpServers": {
"claude-qdrant": {
"command": "npx",
"args": ["claude-qdrant-mcp"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"LM_STUDIO_URL": "http://127.0.0.1:1235",
"EMBEDDING_MODEL": "text-embedding-finetuned-bge-m3",
"EMBEDDING_DIM": "1024",
"CLIENT_COLLECTIONS": "work,personal,research"
}
}
}
}Prompts to try
Example prompts that leverage the semantic search and retrieval tools exposed by this server.
- "Search the 'work' collection for documents about quarterly budget planning."
- "Find all document chunks mentioning API rate limits across all my collections."
- "What does the 'research' collection contain? Show me collection statistics."
- "Search the 'personal' collection for notes about my trip to Japan."
- "Find document summaries related to machine learning across all clients."Troubleshooting Claude Qdrant
Connection to Qdrant fails when starting the server
Ensure Qdrant is running: `docker run -p 6333:6333 qdrant/qdrant`. Confirm QDRANT_URL in .env matches the port you started Qdrant on. For Qdrant Cloud, verify QDRANT_API_KEY is set correctly.
Embedding generation fails or returns errors
Open LM Studio and confirm the BGE-M3 embedding model is loaded and the local server is active on the port specified in LM_STUDIO_URL. The embedding endpoint must be running before seeding or querying documents.
Searches return no results after seeding documents
Run `npm run seed -- --validate-only --client <name> --filesdir ./docs` to check that documents were ingested. Confirm EMBEDDING_DIM matches the dimension the BGE-M3 model actually outputs (default 1024).
Frequently Asked Questions about Claude Qdrant
What is Claude Qdrant?
Claude Qdrant is a Model Context Protocol (MCP) server that local-first typescript mcp server for qdrant with client isolation, lm studio integration, and scalable document workflows. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Claude Qdrant?
Follow the installation instructions on the Claude Qdrant GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Claude Qdrant?
Claude Qdrant works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Claude Qdrant free to use?
Yes, Claude Qdrant is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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