What are the main tools provided?
The server provides `qdrant-store` to store information (with optional JSON metadata) and `qdrant-find` to retrieve relevant information based on a query.
mcp-server-qdrant: A Qdrant MCP server
@qdrant
基本信息
关于 mcp-server-qdrant: A Qdrant MCP server
An official Qdrant Model Context Protocol (MCP) server implementation
mcp-server-qdrant: A Qdrant MCP server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-qdrant": {
"command": "uvx",
"args": [
"mcp-server-qdrant"
]
}
}
}mcp-server-qdrant: A Qdrant MCP server 工具
4`information` (string): Information to store
Confirmation message
`query` (string): Query to use for searching
Information stored in the Qdrant database as separate messages
概览
What is mcp-server-qdrant?
mcp-server-qdrant is an official Model Context Protocol server for Qdrant, the vector search engine. It acts as a semantic memory layer that enables LLM applications to store and retrieve information using vector embeddings. It is built for developers integrating AI tools with persistent, searchable memory.
How to use mcp-server-qdrant?
Configure the server via environment variables (e.g., QDRANT_URL, COLLECTION_NAME, EMBEDDING_MODEL). Run it with uvx mcp-server-qdrant or via Docker. The server exposes two tools: qdrant-store to store information and qdrant-find to retrieve relevant information from Qdrant. It supports stdio, sse, and streamable-http transports.
Key features of mcp-server-qdrant
- Semantic memory layer on top of Qdrant vector search.
- Store and retrieve information with optional metadata.
- Automatic collection creation if it does not exist.
- Configurable embedding model (FastEmbed models only).
- Works with any MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.).
- Customizable tool descriptions via environment variables.
Use cases of mcp-server-qdrant
- Give LLM applications persistent, searchable memory for conversations or knowledge.
- Store and retrieve code snippets with semantic search in Cursor or Windsurf.
- Enable Claude Code to search over existing codebases semantically.
- Build a personal knowledge base that AI assistants can query.
- Share a remote semantic memory server across a team using SSE transport.
常见问题
What embedding models are supported?
Currently only FastEmbed models are supported. The default model is `sentence-transformers/all-MiniLM-L6-v2`.
Can I run the server locally without a remote Qdrant instance?
Yes. Set `QDRANT_LOCAL_PATH` to a local database path; do not set `QDRANT_URL` at the same time.
Which transport protocols are available?
The server supports `stdio` (default), `sse`, and `streamable-http`. Use the `--transport` flag to select one.
Does the server require an API key for Qdrant?
It is optional. You can set `QDRANT_API_KEY` if your Qdrant server requires authentication.
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