by qdrant
Qdrant is a powerful vector database for AI systems to store and retrieve vector-based memories with advanced vector sea
Provides vector-based memory storage and retrieval for AI systems using Qdrant database. Enables AI to store information and find semantically similar content later.
Qdrant is an official MCP server published by qdrant that provides AI assistants with tools and capabilities via the Model Context Protocol. Qdrant is a powerful vector database for AI systems to store and retrieve vector-based memories with advanced vector sea It is categorized under ai ml, databases.
You can install Qdrant in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
Apache-2.0
Qdrant is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
We wired Qdrant into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Qdrant surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Qdrant is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend Qdrant for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Qdrant is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Qdrant reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Qdrant is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Qdrant against two servers with overlapping tools; this profile had the clearer scope statement.
Qdrant is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: Qdrant is the kind of server we cite when onboarding engineers to host + tool permissions.
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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.
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.
qdrant-store
information (string): Information to storemetadata (JSON): Optional metadata to storecollection_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.qdrant-find
query (string): Query to use for searchingcollection_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.The configuration of the server is done using environment variables:
| 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 |
Note: You cannot provide both QDRANT_URL and QDRANT_LOCAL_PATH at the same time.
[!IMPORTANT] Command-line arguments are not supported anymore! Please use environment variables for all configuration.
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_DEBUG | Enable debug mode | false |
FASTMCP_LOG_LEVEL | Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) | INFO |
FASTMCP_HOST | Host address to bind the server to | 127.0.0.1 |
FASTMCP_PORT | Port to run the server on | 8000 |
FASTMCP_WARN_ON_DUPLICATE_RESOURCES | Show warnings for duplicate resources | true |
FASTMCP_WARN_ON_DUPLICATE_TOOLS | Show warnings for duplicate tools | true |
FASTMCP_WARN_ON_DUPLICATE_PROMPTS | Show warnings for duplicate prompts | true |
FASTMCP_DEPENDENCIES | List of dependencies to install in the server environment | [] |
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
The server supports different transport protocols that can be specified using the --transport flag:
QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
uvx mcp-server-qdrant --transport sse
Supported transport protocols:
stdio (default): Standard input/output transport, might only be used by local MCP clientssse: Server-Sent Events transport, perfect for remote clientsstreamable-http: Streamable HTTP transport, perfect for remote clients, more recent than SSEThe default transport is stdio if not specified.
When SSE transport is used, the server will listen on the specified port and wait for incoming connections. The default
port is 8000, however it can be changed using the FASTMCP_PORT environment variable.
QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="my-collection" \
FASTMCP_PORT=1234 \
uvx mcp-server-qdrant --transport sse
A Dockerfile is available for building and running the MCP server:
# Build the container
docker build -t mcp-server-qdrant .
# Run the container
docker run -p 8000:8000 \
-e FASTMCP_HOST="0.0.0.0" \
-e QDRANT_URL="http://your-qdrant-server:6333" \
-e QDRANT_API_KEY="your-api-key" \
-e COLLECTION_NAME="your-collection" \
mcp-server-qdrant
[!TIP] Please note that we set
FASTMCP_HOST="0.0.0.0"to make the server listen on all network interfaces. This is necessary when running the server in a Docker container.
To install Qdrant MCP Server for Claude Desktop automatically via Smithery:
npx @smithery/cli install mcp-server-qdrant --client claude
To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your
claude_desktop_config.json:
{
"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",
"COLLECTION_NAME": "your-collection-name",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}
For local Qdrant mode:
{
"qdrant": {
"command": "uvx",
"args": ["mcp-server-qdrant"],
"env": {
"QDRANT_LOCAL_PATH": "/path/to/qdrant/database",
"COLLECTION_NAME": "your-collection-name",
"EMBEDDING_MODEL": "sentence-transformers/all-MiniLM-L6-v2"
}
}
}
This MCP server will automatically create a collection with the specified name if it doesn't exist.
By default, the server will use the sentence-transformers/all-MiniLM-L6-v2 embedding model to encode memories.
For the time being, only FastEmbed models are supported.
This MCP server can be used with any MCP-compatible client. For example, you can use it with Cursor and VS Code, which provide built-in support for the Model Context Protocol.
You can configure this MCP server to work as a code search tool for Cursor or Windsurf by customizing the tool descriptions:
QDRANT_URL="http://localhost:6333" \
COLLECTION_NAME="code-snippets" \
TOOL_STORE_DESCRIPTION="Store reusable code snippets for later retrieval. \
The 'information' parameter should contain a natural language description of what the code does, \
while the actual code should be included in the 'metadata' parameter as a 'code' property. \
The value of 'metadata' is a Python dictionary with strings as keys. \
Use this whenever you generate some code snippet." \
TOOL_FIND_DESCRIPTION="Search for relevant code snippets based on natural language descriptions. \
The 'query' parameter should describe what you're looking for, \
and the tool will return the most relevant code snippets. \
Use this when you need to find existing code snippets for reuse or reference." \
uvx mcp-server-qdrant --transport sse # Enable SSE transport
In Cursor/Windsurf, you can then configure the MCP server in your settings by pointing to this running server using SSE transport protocol. The description on how to add an MCP server to Cursor can be found in the [Cursor documentation](https://docs.cursor.com/context/model-context-protocol#adding
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ Use when
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid when
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.