Qdrant
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.
What is Qdrant?
Qdrant is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to 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.
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.
This server falls under the Databases category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- MCP server for document ingestion and semantic search on Qdr
Use Cases
Maintainer
Works with
Installation
NPM
npx -y mcp-server-qdrantManual Installation
npx -y mcp-server-qdrantConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Qdrant
The mcp-server-qdrant MCP server connects your AI assistant to a Qdrant vector database, enabling persistent semantic memory storage and retrieval across sessions. It exposes two core tools—qdrant-store and qdrant-find—that let the AI embed and index information using local FastEmbed or OpenAI-compatible models, then search it with natural language queries. Developers use this server to build long-term memory for agents, create searchable code-snippet libraries, and enable semantic retrieval over documentation without maintaining a separate search pipeline.
Prerequisites
- Python 3.10+ with uvx (via uv) installed, or Docker for container-based deployment
- A running Qdrant instance: either a local server at http://localhost:6333, or a Qdrant Cloud cluster URL and API key
- An MCP client such as Claude Desktop, Cursor, or Windsurf
- Optional: an OpenAI-compatible embedding model API key if you prefer cloud embeddings over the default local FastEmbed model
Install or start Qdrant
Run a local Qdrant instance via Docker or sign up for Qdrant Cloud. The MCP server needs a reachable Qdrant endpoint before it can store or search vectors.
docker run -p 6333:6333 qdrant/qdrantInstall mcp-server-qdrant via uvx
The server is distributed as a Python package on PyPI and is best run with uvx, which creates an isolated environment automatically. No global install is required.
uvx mcp-server-qdrantSet required environment variables
Configure the server by setting environment variables. QDRANT_URL points to your Qdrant instance. COLLECTION_NAME is the vector collection to use (it will be created automatically if it does not exist). EMBEDDING_MODEL selects the local FastEmbed model.
export QDRANT_URL="http://localhost:6333"
export COLLECTION_NAME="my-collection"
export EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"Add the server to your MCP client configuration
Edit your Claude Desktop config file (claude_desktop_config.json) to register the server. Pass environment variables directly in the config so the server starts with the correct settings each time.
Verify the connection
Open your MCP client and ask it to store a test note. The qdrant-store tool should succeed without errors, confirming the server can reach your Qdrant instance and create the collection.
Qdrant Examples
Client configuration (local Qdrant)
Claude Desktop configuration using uvx with a local Qdrant instance and the default FastEmbed model.
{
"mcpServers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"COLLECTION_NAME": "my-collection",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}
}Client configuration (Qdrant Cloud)
Claude Desktop configuration pointing to a Qdrant Cloud cluster with API key authentication.
{
"mcpServers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_URL": "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
"QDRANT_API_KEY": "your_api_key_here",
"COLLECTION_NAME": "project-memory",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}
}Prompts to try
Example prompts that use the qdrant-store and qdrant-find tools to build semantic memory.
- "Remember this architecture decision: we use event sourcing for the orders service because it simplifies auditing."
- "Search my notes for anything about the orders service architecture."
- "Store this code snippet for parsing ISO dates in Python: [paste snippet]. Tag it as 'python, datetime'."
- "Find all code snippets related to date parsing."
- "Remember that the staging API endpoint is https://api-staging.example.com and the prod endpoint is https://api.example.com."
- "What API endpoints have I stored?"Troubleshooting Qdrant
Connection refused error when the server tries to reach Qdrant
Confirm your Qdrant container or cloud instance is running and that QDRANT_URL is correct. For Docker, ensure the port mapping -p 6333:6333 is in place and no firewall blocks the port.
Collection not found or embedding dimension mismatch after changing EMBEDDING_MODEL
Changing the embedding model changes the vector dimension. Delete the existing collection in Qdrant (or use a new COLLECTION_NAME) so a fresh collection with the correct dimension is created on the next store operation.
uvx command not found
Install uv first with 'curl -LsSf https://astral.sh/uv/install.sh | sh', then reload your shell. uvx is bundled with uv and provides isolated Python tool execution.
Frequently Asked Questions about Qdrant
What is Qdrant?
Qdrant is a Model Context Protocol (MCP) server that 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. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Qdrant?
Install via npm with the command: npx -y mcp-server-qdrant. Then add the server configuration to your AI client's JSON config file (e.g., claude_desktop_config.json or .cursor/mcp.json).
Which AI clients work with Qdrant?
Qdrant works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Qdrant free to use?
Yes, Qdrant is open source and available under the Apache 2.0 license. You can use it freely in both personal and commercial projects.
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