Semantic search and memory storage via the Qdrant vector database. Store information with embeddings, retrieve by meaning, and give your AI agent long-term semantic memory. Supports local and cloud Qdrant instances.
Install via CLI
mog install mogteam/mcp-qdrantManual config
Qdrant instance URL (e.g., http://localhost:6333 or your cloud URL)
Qdrant API key (required for Qdrant Cloud)
Qdrant collection name for storing memories
.cursor/mcp.json{
"mcpServers": {
"mogteam-mcp-qdrant": {
"command": "uvx",
"args": [
"mcp-server-qdrant"
],
"env": {
"QDRANT_URL": "<QDRANT_URL>",
"QDRANT_API_KEY": "<your-secret>",
"COLLECTION_NAME": "<COLLECTION_NAME>"
}
}
}
}Or run mog install to configure automatically. MCP docs
Semantic search and memory storage via the Qdrant vector database. Store information with embeddings, retrieve by meaning, and give your AI agent long-term semantic memory. Supports local and cloud Qdrant instances.
mog install mogteam/mcp-qdrant
Requires Python and
uv. Install with:pip install uvorbrew install uv.
qdrant vector-db embeddings semantic-search memory mcp
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@mogteam
Official curated skills and prompt templates from the mog.md team.
Source
Qdrant (official)
github.com/qdrant/mcp-server-qdrantThis package is sourced from the above repository and distributed on mog under its original license.
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