The Better Qdrant MCP Server manages Qdrant vector databases through semantic search and document operations.
Core Capabilities:
List Collections - View all available Qdrant collections in your database
Add Documents - Process and ingest documents from file paths into collections with configurable chunking (chunk size and overlap)
Semantic Search - Query collections to find similar documents based on semantic meaning with customizable result limits
Delete Collections - Remove collections from your Qdrant database
Embedding Service Support:
OpenAI - Cloud-based embeddings via OpenAI API
OpenRouter - Cloud-based embeddings via OpenRouter API
Ollama - Local embeddings using Ollama models (default: nomic-embed-text)
FastEmbed - Local embeddings using FastEmbed models
Utilizes .env files for configuration of API keys, endpoints, and other server settings.
Requires Node.js runtime environment (version 18.0.0 or higher) to operate the MCP server.
Integrates with Ollama for local embedding models, supporting document embedding and semantic search functionality.
Leverages OpenAI's embedding capabilities for processing and semantically searching documents in Qdrant collections.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Better Qdrant MCP Server search for documents about machine learning in my research collection"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for enhanced Qdrant vector database functionality. This server provides tools for managing Qdrant collections, adding documents, and performing semantic searches.
List Collections: View all available Qdrant collections
Add Documents: Process and add documents to a Qdrant collection with various embedding services
Search: Perform semantic searches across your vector database
Delete Collection: Remove collections from your Qdrant database
Related MCP server: doc-lib-mcp
npm install -g better-qdrant-mcp-serverOr use it directly with npx:
npx better-qdrant-mcp-serverThe server uses environment variables for configuration. You can set these in a .env file in your project root:
# Qdrant Configuration
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your_api_key_if_needed
# Embedding Service API Keys
OPENAI_API_KEY=your_openai_api_key
OPENROUTER_API_KEY=your_openrouter_api_key
OLLAMA_ENDPOINT=http://localhost:11434OpenAI: Requires an API key
OpenRouter: Requires an API key
Ollama: Local embedding models (default endpoint: http://localhost:11434)
FastEmbed: Local embedding models
To use this MCP server with Claude, add it to your MCP settings configuration file:
{
"mcpServers": {
"better-qdrant": {
"command": "npx",
"args": ["better-qdrant-mcp-server"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"QDRANT_API_KEY": "your_api_key_if_needed",
"DEFAULT_EMBEDDING_SERVICE": "ollama",
"OPENAI_API_KEY": "your_openai_api_key",
"OPENAI_ENDPOINT": "https://api.openai.com/v1",
"OPENROUTER_API_KEY": "your_openrouter_api_key",
"OPENROUTER_ENDPOINT": "https://api.openrouter.com/v1",
"OLLAMA_ENDPOINT": "http://localhost:11434",
"OLLAMA_MODEL": "nomic-embed-text"
}
}
}
}use_mcp_tool
server_name: better-qdrant
tool_name: list_collections
arguments: {}use_mcp_tool
server_name: better-qdrant
tool_name: add_documents
arguments: {
"filePath": "/path/to/your/document.pdf",
"collection": "my-collection",
"embeddingService": "openai",
"chunkSize": 1000,
"chunkOverlap": 200
}use_mcp_tool
server_name: better-qdrant
tool_name: search
arguments: {
"query": "your search query",
"collection": "my-collection",
"embeddingService": "openai",
"limit": 5
}use_mcp_tool
server_name: better-qdrant
tool_name: delete_collection
arguments: {
"collection": "my-collection"
}Node.js >= 18.0.0
A running Qdrant server (local or remote)
API keys for the embedding services you want to use
MIT
Add documents to a Qdrant collection with specified embedding service
Inputs: collection (required), embeddingService (required), filePath (required), chunkOverlap, +1 more.
Delete a Qdrant collection
Inputs: collection (required).
List all available Qdrant collections
Inputs: no named parameters.
Search for similar documents in a collection
Inputs: collection (required), embeddingService (required), query (required), limit.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 4 tools
Each tool has a clearly distinct purpose: add_documents handles document insertion, delete_collection removes collections, list_collections enumerates them, and search performs similarity queries. There is no overlap in functionality, making tool selection straightforward.
All tools follow a consistent verb_noun pattern (add_documents, delete_collection, list_collections, search). The naming is uniform and predictable, with no deviations in style or convention.
With only 4 tools, the set feels thin for a vector database server, lacking operations like collection creation, document updates, or deletion. While the tools cover basic CRUD aspects, the scope is limited and may require workarounds for full functionality.
There are significant gaps in the tool surface for a Qdrant server: no create_collection tool, no update or delete document operations, and no management of embeddings or configurations. This incomplete coverage will likely cause agent failures in common workflows.
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