Build your own Qdrant Vector Store MCP server

This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation. This n8n implementation exposes other cool API features from Qdrant such as facet search, grouped search and recommendations APIs. With this, we can build an easily customisable and maintainable Qdrant MCP server for business intelligence. This MCP example is based off an official MCP reference implementation which can be found here - https://github.com/qdrant/mcp-server-qdrant How it works A MCP server trigger is used and connected to 5 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task. We use a mix

Help me set up the n8n workflow "Build your own Qdrant Vector Store MCP server" (https://n8n.io/workflows/3636). It uses: HTTP Request, Code, Embeddings OpenAI, Recursive Character Text Splitter, Call n8n Workflow Tool, Default Data Loader, Qdrant Vector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.