mcp-server-qdrant: A Qdrant MCP server

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Overview

An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database.

Components

Tools

1. qdrant-store
   • Store some information in the Qdrant database
   • Input:
     • information (string): Information to store
     • metadata (JSON): Optional metadata to store
     • collection_name (string): Name of the collection to store the information in. This field is required if there are no default collection name. If there is a default collection name, this field is not enabled.
   • Returns: Confirmation message
2. qdrant-find
   • Retrieve relevant information from the Qdrant database
   • Input:
     • query (string): Query to use for searching
     • collection_name (string): Name of the collection to store the information in. This field is required if there are no default collection name. If there is a default collection name, this field is not enabled.
   • Returns: Information stored in the Qdrant database as separate messages

Environment Variables

Configuration is done via environment variables. The only command-line argument is --transport, used to select the transport protocol.

[!NOTE] You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.

Name	Description	Default Value
QDRANT_URL	URL of the Qdrant server	None
QDRANT_API_KEY	API key for the Qdrant server	None
COLLECTION_NAME	Name of the default collection to use.	None
QDRANT_LOCAL_PATH	Path to the local Qdrant database (alternative to QDRANT_URL)	None
EMBEDDING_PROVIDER	Embedding provider to use (currently only "fastembed" is supported)	fastembed
EMBEDDING_MODEL	Name of the embedding model to use	sentence-transformers/all-MiniLM-L6-v2
TOOL_STORE_DESCRIPTION	Custom description for the store tool	See default in settings.py
TOOL_FIND_DESCRIPTION	Custom description for the find tool	See default in settings.py
QDRANT_SEARCH_LIMIT	Maximum number of results to return from search	10
QDRANT_READ_ONLY	Enable read-only mode (disables qdrant-store tool)	false

FastMCP Environment Variables

Since mcp-server-qdrant is based on FastMCP, it also supports all the FastMCP environment variables. The most important ones are listed below:

Environment Variable	Description	Default Value
FASTMCP_LOG_LEVEL	Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)	INFO
FASTMCP_SERVER_DEBUG	Enable debug mode	false
FASTMCP_SERVER_HOST	Host address to bind the server to	127.0.0.1
FASTMCP_SERVER_PORT	Port to run the server on	8000
FASTMCP_SERVER_ON_DUPLICATE_RESOURCES	Behavior for duplicate resources (warn, error, replace, ignore)	warn
FASTMCP_SERVER_ON_DUPLICATE_TOOLS	Behavior for duplicate tools (warn, error, replace, ignore)	warn
FASTMCP_SERVER_ON_DUPLICATE_PROMPTS	Behavior for duplicate prompts (warn, error, replace, ignore)	warn
FASTMCP_SERVER_DEPENDENCIES	List of dependencies to install in the server environment	[]

[!NOTE] Server-specific settings use the FASTMCP_SERVER_ prefix. This may change in future versions.

Installation

Using uvx

When using uvx no specific installation is needed to directly run mcp-server-qdrant.

QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" \
uvx mcp-server-qdrant

Transport Protocols

The server supports different transport protocols t