Qdrant MCP Server
✓ verifiedStore and search vector embeddings as a semantic memory.
Don't have ToolPiper? We'll take you to the download.
Publisher
License
Apache-2.0
Transport
Local subprocess
Tools
2
What this server does
Qdrant is a vector database; this server turns it into a semantic memory the assistant can write to and search. Store snippets of text with metadata, then retrieve the most relevant ones by meaning rather than keyword. It is the building block for long-term recall across conversations: save decisions, facts, or code patterns once, and the model finds them later by similarity. Works against a local Qdrant or a Qdrant Cloud cluster. The server handles embedding generation with a sensible default model.
Tools included
qdrant_store
Store a piece of text with its embedding and metadata.
qdrant_find
Find the most relevant stored entries for a query.
The authoritative list comes from the server's tools/list at runtime. This catalog entry may lag the upstream until the next ToolPiper release.
Install in ToolPiper
ToolPiper handles the JSON configuration. Click Install, fill in any required tokens, and the server is ready in your chat.
Don't have ToolPiper? We'll take you to the download.
Install in another client
Install in another MCP client (Claude Desktop, Cursor, Continue, Cline)
Add this to ~/Library/Application Support/Claude/claude_desktop_config.json.
{
"mcpServers": {
"qdrant": {
"command": "uvx",
"args": [
"mcp-server-qdrant"
]
}
}
}Example prompts
"Store this architecture decision so we can recall it later."
"Find anything we have saved about how the auth service handles token refresh."
"What did we decide about the database migration strategy?"
Security notes
For Qdrant Cloud the API key gates access to your cluster; treat it as a credential. The collection holds whatever text the assistant stores, in plaintext, so be deliberate about what gets written there, the same way you would with any memory store. A local instance needs no key but is only as secure as the machine it runs on.
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